Daily Papers

We propose two medium-baseline, kiloton-scale neutrino experiments to study neutrinos from LHC proton-proton collisions: SINE, a surface-based scintillator panel detector observing muon neutrinos from the CMS interaction point, and UNDINE, a water Cherenkov detector submerged in lake Geneva observing all-flavor neutrinos from LHCb. Using a Monte Carlo simulation, we estimate millions of neutrino interactions during the high-luminosity LHC era. We show that these datasets can constrain neutrino cross sections, charm production in pp collisions, and strangeness enhancement as a solution to the cosmic-ray muon puzzle. SINE and UNDINE thus offer a cost-effective medium-baseline complement to the proposed short-baseline forward physics facility.

High to ultrahigh energy neutrino detectors can uniquely probe the properties of dark matter χ by searching for the secondary products produced through annihilation and/or decay processes. We evaluate the sensitivities to dark matter thermally averaged annihilation cross section langleσvrangle and partial decay width into neutrinos Γ_{χrightarrowνbarν} (in the mass scale 10^7 leq m_χ/{rm GeV} leq 10^{15}) for next generation observatories like POEMMA and GRAND. We show that in the range 10^7 leq m_χ/{rm GeV} leq 10^{11}, space-based Cherenkov detectors like POEMMA have the advantage of full-sky coverage and rapid slewing, enabling an optimized dark matter observation strategy focusing on the Galactic center. We also show that ground-based radio detectors such as GRAND can achieve high sensitivities and high duty cycles in radio quiet areas. We compare the sensitivities of next generation neutrino experiments with existing constraints from IceCube and updated 90\% C.L. upper limits on langleσvrangle and Γ_{χrightarrowνbarν} using results from the Pierre Auger Collaboration and ANITA. We show that in the range 10^7 leq m_χ/{rm GeV} leq 10^{11} POEMMA and GRAND10k will improve the neutrino sensitivity to particle dark matter by factors of 2 to 10 over existing limits, whereas GRAND200k will improve this sensitivity by two orders of magnitude. In the range 10^{11} leq m_χ/{rm GeV} leq 10^{15}, POEMMA's fluorescence observation mode will achieve an unprecedented sensitivity to dark matter properties. Finally, we highlight the importance of the uncertainties related to the dark matter distribution in the Galactic halo, using the latest fit and estimates of the Galactic parameters.

One of the current goals of neutrino experiments is to precisely determine standard unknown oscillation parameters such as the leptonic CP phase and mass hierarchy. Lorentz invariance violation represents a potential physics factor that could influence the experiment's ability to achieve these precise determinations. This study investigates the influence of Lorentz invariance violation (LIV) on oscillation dynamics, particularly through non-isotropic CPT-violating (a^{X}_{emu}, a^{X}_{etau}, a^{X}_{mutau}) and CPT-conserving (c^{XY}_{emu}, c^{XY}_{e tau}, c^{XY}_{mu tau}) parameters within the Deep Underground Neutrino Experiment (DUNE). We analyze the impact of these parameters on the mass hierarchy (MH) and Dirac CP phase sensitivity measurements. Our findings indicate that while MH sensitivity remains relatively unaffected, only the presence of c^{XY}_{mu tau} significantly deteriorates MH sensitivity, albeit remaining above the 5 sigma threshold. Additionally, we observe a substantial compromise in CP sensitivity due to the c^{XY}_{e mu} and c^{XY}_{e tau} parameters.

The first direct detection of neutrinos at the LHC not only marks the beginning of a novel collider neutrino program at CERN but also motivates considering additional neutrino detectors to fully exploit the associated physics potential. We investigate the feasibility and physics potential of neutrino experiments located at the surface-level. A topographic desk study was performed to identify all points at which the LHC's neutrino beams exit the earth. The closest location lies about 9 km east of the CMS interaction point, at the bottom of Lake Geneva. Several detectors to be placed at this location are considered, including a water Cherenkov detector and an emulsion detector. The detector concepts are introduced, and projections for their contribution to the LHC forward neutrino program and searches for dark sector particles are presented. However, the dilution of the neutrino flux over distance reduces the neutrino yield significantly, limiting the physics potential of surface-level detectors compared to ones closer to the interaction point, including the proposed FPF.

Collider-testable type I seesaw extensions of the Standard Model are generally protected by an approximate lepton number (LN) symmetry. Consequently, they predict pseudo-Dirac heavy neutral leptons (HNLs) composed of two nearly degenerate Majorana fields. The interference between the two mass eigenstates can induce heavy neutrino-antineutrino oscillations (NNOs) leading to observable lepton number violation (LNV), even though the LN symmetry is approximately conserved. These NNOs could be resolved in long-lived HNL searches at collider experiments, such as the proposed Future Circular e^+e^- Collider (FCC-ee) or Circular Electron Positron Collider (CEPC). However, during their Z-pole runs, the LN carried away by the light (anti)neutrinos produced alongside the HNLs prevents LNV from being observed directly. Nevertheless, NNOs materialise as oscillating signatures in final state distributions. We discuss and compare a selection of such oscillating observables, and perform a Monte Carlo simulation to assess the parameter space in which NNOs could be resolved.

This paper presents the development and characterization of a fiber laser hydrophone designed for deep-sea applications, with a focus on detecting neutrino interactions via their acoustic signatures. The hydrophone design includes a static pressure compensation mechanism, ensuring reliable operation at depths exceeding 1 km. The performance of the hydrophone was evaluated through laboratory tests and experiments in an anechoic basin, where its transfer function was measured before and after a 140-bar pressure cycle. The results show that the hydrophone maintains its sensitivity, with resonance peaks identified in both low- and high-frequency ranges. The hydrophone's sensitivity to acoustic signals was also compared to ambient sea state noise levels, demonstrating compatibility with the lowest noise conditions.

Every second greater than 10^{25} antineutrinos radiate to space from Earth, shining like a faint antineutrino star. Underground antineutrino detectors have revealed the rapidly decaying fission products inside nuclear reactors, verified the long-lived radioactivity inside our planet, and informed sensitive experiments for probing fundamental physics. Mapping the anisotropic antineutrino flux and energy spectrum advance geoscience by defining the amount and distribution of radioactive power within Earth while critically evaluating competing compositional models of the planet. We present the Antineutrino Global Map 2015 (AGM2015), an experimentally informed model of Earth's surface antineutrino flux over the 0 to 11 MeV energy spectrum, along with an assessment of systematic errors. The open source AGM2015 provides fundamental predictions for experiments, assists in strategic detector placement to determine neutrino mass hierarchy, and aids in identifying undeclared nuclear reactors. We use cosmochemically and seismologically informed models of the radiogenic lithosphere/mantle combined with the estimated antineutrino flux, as measured by KamLAND and Borexino, to determine the Earth's total antineutrino luminosity at 3.4^{+2.3}_{-2.2} times 10^{25} nu_e. We find a dominant flux of geo-neutrinos, predict sub-equal crust and mantle contributions, with sim1% of the total flux from man-made nuclear reactors.

Liquid argon time projection chambers (LArTPCs) provide dense, high-fidelity 3D measurements of particle interactions and underpin current and future neutrino and rare-event experiments. Physics reconstruction typically relies on complex detector-specific pipelines that use tens of hand-engineered pattern recognition algorithms or cascades of task-specific neural networks that require extensive, labeled simulation that requires a careful, time-consuming calibration process. We introduce Panda, a model that learns reusable sensor-level representations directly from raw unlabeled LArTPC data. Panda couples a hierarchical sparse 3D encoder with a multi-view, prototype-based self-distillation objective. On a simulated dataset, Panda substantially improves label efficiency and reconstruction quality, beating the previous state-of-the-art semantic segmentation model with 1,000times fewer labels. We also show that a single set-prediction head 1/20th the size of the backbone with no physical priors trained on frozen outputs from Panda can result in particle identification that is comparable with state-of-the-art (SOTA) reconstruction tools. Full fine-tuning further improves performance across all tasks.

Neutron tagging in Gadolinium-doped water may play a significant role in reducing backgrounds from atmospheric neutrinos in next generation proton-decay searches using megaton-scale Water Cherenkov detectors. Similar techniques might also be useful in the detection of supernova neutrinos. Accurate determination of neutron tagging efficiencies will require a detailed understanding of the number of neutrons produced by neutrino interactions in water as a function of momentum transferred. We propose the Atmospheric Neutrino Neutron Interaction Experiment (ANNIE), designed to measure the neutron yield of atmospheric neutrino interactions in gadolinium-doped water. An innovative aspect of the ANNIE design is the use of precision timing to localize interaction vertices in the small fiducial volume of the detector. We propose to achieve this by using early production of LAPPDs (Large Area Picosecond Photodetectors). This experiment will be a first application of these devices demonstrating their feasibility for Water Cherenkov neutrino detectors.

Neutron tagging in Gadolinium-doped water may play a significant role in reducing backgrounds from atmospheric neutrinos in next generation proton-decay searches using megaton-scale Water Cherenkov detectors. Similar techniques might also be useful in the detection of supernova neutrinos. Accurate determination of neutron tagging efficiencies will require a detailed understanding of the number of neutrons produced by neutrino interactions in water as a function of momentum transferred. We propose the Atmospheric Neutrino Neutron Interaction Experiment (ANNIE), designed to measure the neutron yield of atmospheric neutrino interactions in gadolinium-doped water. An innovative aspect of the ANNIE design is the use of precision timing to localize interaction vertices in the small fiducial volume of the detector. We propose to achieve this by using early production of LAPPDs (Large Area Picosecond Photodetectors). This experiment will be a first application of these devices demonstrating their feasibility for Water Cherenkov neutrino detectors.

In this paper we introduce the concept of what we call "NUDAR" (NeUtrino Direction and Ranging), making the point that measurements of the observed energy and direction vectors can be employed to passively deduce the exact three-dimensional location and thermal power of geophysical and anthropogenic neutrino sources from even a single detector. We present the most precise background estimates to date, all handled in full three dimensions, as functions of depth and geographical location. For the present calculations, we consider a hypothetical 138 kiloton detector which can be transported to an ocean site and deployed to an operational depth. We present a Bayesian estimation framework to incorporate any a priori knowledge of the reactor that we are trying to detect, as well as the estimated uncertainty in the background and the oscillation parameters. Most importantly, we fully employ the knowledge of the reactor spectrum and the distance-dependent effects of neutrino oscillations on such spectra. The latter, in particular, makes possible determination of range from one location, given adequate signal statistics. Further, we explore the rich potential of improving detection with even modest improvements in individual neutrino direction determination. We conclude that a 300 MWth reactor can indeed be geolocated, and its operating power estimated with one or two detectors in the hundred kiloton class at ranges out to a few hundred kilometers. We note that such detectors would have natural and non-interfering utility for scientific studies of geo-neutrinos, neutrino oscillations, and astrophysical neutrinos. This motivates the development of cost effective methods of constructing and deploying such next generation detectors.

We describe a new detector, called NuLat, to study electron anti-neutrinos a few meters from a nuclear reactor, and search for anomalous neutrino oscillations. Such oscillations could be caused by sterile neutrinos, and might explain the "Reactor Antineutrino Anomaly". NuLat, is made possible by a natural synergy between the miniTimeCube and mini-LENS programs described in this paper. It features a "Raghavan Optical Lattice" (ROL) consisting of 3375 boron or ^6Li loaded plastic scintillator cubical cells 6.3\,cm (2.500") on a side. Cell boundaries have a 0.127\,mm (0.005") air gap, resulting in total internal reflection guiding most of the light down the 3 cardinal directions. The ROL detector technology for NuLat gives excellent spatial and energy resolution and allows for in-depth event topology studies. These features allow us to discern inverse beta decay (IBD) signals and the putative oscillation pattern, even in the presence of other backgrounds. We discuss here test venues, efficiency, sensitivity and project status.

Ultra-high-energy neutrinos and cosmic rays are excellent probes of astroparticle physics phenomena. For astroparticle physics analyses, robust and accurate reconstruction of signal parameters such as arrival direction and energy is essential. Radio detection is an established detector concept explored by many observatories; however, current reconstruction methods ignore bin-to-bin noise correlations, which limits reconstruction resolution and, so far, has prevented calculations of event-by-event uncertainties. In this work, we present a likelihood description of neutrino or cosmic-ray signals in radio detectors with correlated noise, as present in all neutrino and cosmic-ray radio detectors. We demonstrate, with simulation studies of both neutrinos and cosmic-ray radio signals, that signal parameters such as energy and direction, including event-by-event uncertainties with correct coverage, can be obtained. This method reduces reconstruction uncertainties and biases compared to previous approaches. Additionally, the Likelihood can be used for event selection and enables differentiable end-to-end detector optimization. The reconstruction code is available through the open-source software NuRadioReco.

The OSIRIS detector is a subsystem of the liquid scintillator fillling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of 10^{-16} g/g of ^{238}U and ^{232}Th requires a large (sim20 m^3) detection volume and ultralow background levels. The present paper reports on the design and major components of the OSIRIS detector, the detector simulation as well as the measuring strategies foreseen and the sensitivity levels to U/Th that can be reached in this setup.

We present a search for the diffuse extremely-high-energy neutrino flux using 12.6 years of IceCube data. The non-observation of neutrinos with energies well above 10 , PeV constrains the all-flavor neutrino flux at 10^{18} , eV to a level of E^2 Phi_{nu_e + nu_mu + nu_tau} simeq 10^{-8} , GeV , cm^{-2} , s^{-1} , sr^{-1}, the most stringent limit to date. Using this data, we constrain the proton fraction of ultra-high-energy cosmic rays (UHECRs) above simeq 30 , EeV to be lesssim 70,% (at 90,% CL) if the cosmological evolution of the sources is comparable to or stronger than the star formation rate. This result complements direct air-shower measurements by being insensitive to uncertainties associated with hadronic interaction models. It is the first such result to disfavor the ``proton-only" hypothesis for UHECRs using neutrino data.

Neutrino self-interactions beyond the standard model have profound implications in astrophysics and cosmology. In this work, we study an uncharted scenario in which one of the three neutrino species has a mass much smaller than the temperature of the cosmic neutrino background. This results in a relativistic component that significantly broadens the absorption feature on the astrophysical neutrino spectra, in contrast to the sharply peaked absorption expected in the extensively studied scenarios assuming a fully nonrelativistic cosmic neutrino background. By solving the Boltzmann equations for neutrino absorption and regeneration, we demonstrate that this mechanism provides novel sensitivity to sub-keV mediator masses, well below the traditional sim 1--100 MeV range. Future observations of the diffuse supernova neutrino background with Hyper-Kamiokande could probe coupling strengths down to g sim 10^{-8}, surpassing existing constraints by orders of magnitude. These findings open new directions for discoveries and offer crucial insights into the interplay between neutrinos and the dark sector.

An ultra high energy electromagnetic cascade, a purely leptonic process and initiated by either photons or e^pm, can be a source of high energy neutrinos. We present a public python3 code, MUNHECA, to compute the neutrino spectrum by taking into account various QED processes, with the cascade developing either along the propagation in the cosmic microwave background in the high-redshift universe or in a predefined photon background surrounding the astrophysical source. The user can adjust various settings of MUNHECA, including the spectrum of injected high energy photons, the background photon field and the QED processes governing the cascade evolution. We improve the modeling of several processes, provide examples of the execution of MUNHECA and compare it with some earlier and more simplified estimates of the neutrino spectrum from electromagnetic cascades.

In 2013, the IceCube collaboration announced the detection of a diffuse high-energy astrophysical neutrino flux. The origin of this flux is still largely unknown. The most significant individual source is the close-by Seyfert galaxy NGC 1068 at 4.2-sigma level with a soft spectral index. To identify sources based on their counterpart, IceCube releases realtime alerts corresponding to neutrinos with a high probability of astrophysical origin. We report here the spatial coincidence of two neutrino alerts, IC220424A and IC230416A, with the Seyfert galaxy NGC 7469 at a distance of 70 Mpc. We evaluate, a-posteriori, the chance probability of such a coincidence and discuss this source as a potential neutrino emitter based on its multi-wavelength properties and in comparison to NGC 1068 by performing a Goodness-of-Fit test. The test statistic is derived from a likelihood ratio that includes the neutrino angular uncertainty and the source distance. We apply this test first to a catalog of AGN sources and second to a catalog of Seyfert galaxies only. Our a-posteriori evaluation excludes the possibility of an accidental spatial coincidence of both neutrinos with the Seyfert galaxy NGC 7469 at 3.2-sigma level, leaving open the possibility that either one or both neutrinos originated from the source. To be compatible with non-detections of TeV neutrinos, the source would need to have a hard spectral index.

This article presents a review about the main CERN n\_TOF contributions to the field of neutron-capture experiments of interest for s-process nucleosynthesis studies over the last 25 years, with special focus on the measurement of radioactive isotopes. A few recent capture experiments on stable isotopes of astrophysical interest are also discussed. Results on s-process branching nuclei are appropriate to illustrate how advances in detection systems and upgrades in the facility have enabled increasingly challenging experiments and, as a consequence, have led to a better understanding and modeling of the s-process mechanism of nucleosynthesis. New endeavors combining radioactive-ion beams from ISOLDE for the production of radioisotopically pure samples for activation experiments at the new NEAR facility at n\_TOF are briefly discussed. On the basis of these new exciting results, also current limitations of state-of-the-art TOF and activation techniques will be depicted, thereby showing the pressing need for further upgrades and enhancements on both facilities and detection systems. A brief account of the potential technique based on inverse kinematics for direct neutron-capture measurements is also presented.

We consider the flavor transformation of neutrinos through oscillatory matter profiles. We show that the neutrino oscillation Hamiltonian in this case describes a Rabi system with an infinite number of Rabi modes. We further show that, in a given physics problem, the majority of the Rabi modes have too small amplitudes to be relevant. We also go beyond the rotating wave approximation and derive the relative detuning of the Rabi resonance when multiple Rabi modes with small amplitudes are present. We provide an explicit criterion of whether an off-resonance Rabi mode can affect the parametric flavor transformation of the neutrino.

Pulsar wind nebulae (PWNe), especially the young ones, are among the most energetic astrophysical sources in the Galaxy. It is usually believed that the spin-down energy injected from the pulsars is converted into magnetic field and relativistic electrons, but the possible presence of proton acceleration inside PWNe cannot be ruled out. Previous works have estimated the neutrino emission from PWNe using various source catalogs measured in gamma-rays. However, such results rely on the sensitivity of TeV gamma-ray observations and may omit the contribution by unresolved sources. Here we estimate the potential neutrino emission from a synthetic population of PWNe in the Galaxy with a focus on the ones that are still in the free expansion phase. In the calculation, we model the temporal evolution of the free-expanding PWNe and consider the transport of protons inside the PWNe. The Crab nebula is treated as a standard template for young PWNe to evaluate some model parameters, such as the energy conversion fraction of relativistic protons and the target gas density for the hadronic process, which are relevant to neutrino production. In the optimistic case, the neutrino flux from the simulated young PWNe may constitute to 5% of the measured flux by IceCube around 100 TeV. At higher energy around 1 PeV, the neutrino emission from the population highly depends on the injection spectral shape, and also on the emission of the nearby prominent sources.

Rapid advancement of machine learning solutions has often coincided with the production of a test public data set. Such datasets reduce the largest barrier to entry for tackling a problem -- procuring data -- while also providing a benchmark to compare different solutions. Furthermore, large datasets have been used to train high-performing feature finders which are then used in new approaches to problems beyond that initially defined. In order to encourage the rapid development in the analysis of data collected using liquid argon time projection chambers, a class of particle detectors used in high energy physics experiments, we have produced the PILArNet, first 2D and 3D open dataset to be used for a couple of key analysis tasks. The initial dataset presented in this paper contains 300,000 samples simulated and recorded in three different volume sizes. The dataset is stored efficiently in sparse 2D and 3D matrix format with auxiliary information about simulated particles in the volume, and is made available for public research use. In this paper we describe the dataset, tasks, and the method used to procure the sample.

Future HEP experiments present stringent challenges to calorimeter materials in radiation tolerance, time response and project cost. The 2019 report of the DOE Basic Research Needs Study on High Energy Physics Detector Research and Development points out three priority research directions for future calorimetry. Following these research directions letters of interest were submitted to the Snowmass organized by the Division of Particles and Fields of the American Physics Society. This report summarizes materials to be developed in the form of inorganic, liquid (oil- and water-based), and plastic scintillators and wavelength shifters to advance HEP calorimetry to face the challenges in radiation hardness, fast timing, and cost-effectiveness. Some of these materials may also find applications for future HEP time-of-flight system, and beyond HEP in nuclear physics, hard X-ray imaging and medical instruments.

We present the development of the miniTimeCube (mTC), a novel compact neutrino detector. The mTC is a multipurpose detector, aiming to detect not only neutrinos but also fast/thermal neutrons. Potential applications include the counterproliferation of nuclear materials and the investigation of antineutrino short-baseline effects. The mTC is a plastic 0.2% ^{10}B - doped scintillator (13 cm)^3 cube surrounded by 24 Micro-Channel Plate (MCP) photon detectors, each with an 8times8 anode totaling 1536 individual channels/pixels viewing the scintillator. It uses custom-made electronics modules which mount on top of the MCPs, making our detector compact and able to both distinguish different types of events and reject noise in real time. The detector is currently deployed and being tested at the National Institute of Standards and Technology (NIST) Center for Neutron Research (NCNR) nuclear reactor (20 MW_th) in Gaithersburg, MD. A shield for further tests is being constructed, and calibration and upgrades are ongoing. The mTC's improved spatiotemporal resolution will allow for determination of incident particle directions beyond previous capabilities.

The J-PARC Hadron Experimental Facility was constructed with an aim to explore the origin and evolution of matter in the universe through the experiments with intense particle beams. In the past decade, many results on particle and nuclear physics have been obtained at the present facility. To expand the physics programs to unexplored regions never achieved, the extension project of the Hadron Experimental Facility has been extensively discussed. This white paper presents the physics of the extension of the Hadron Experimental Facility for resolving the issues in the fields of the strangeness nuclear physics, hadron physics, and flavor physics.

Ultra-high energy neutrinos (E>10^{17} eV) can provide insight into the most powerful accelerators in the universe, however their flux is extremely low. The Beamforming Elevated Array for COsmic Neutrinos (BEACON) is a detector concept which efficiently achieves sensitivity to this flux by employing phased radio arrays on mountains, which search for the radio emission of up-going extensive air showers created by Earth-skimming tau neutrinos. Here, we calculate the point-source effective area of BEACON and characterize its sensitivity to transient neutrino fluences with both short (<15 min) and long (> 1 day) durations. Additionally, by integrating the effective area, we provide an updated estimate of the diffuse flux sensitivity. With just 100 stations, BEACON achieves sensitivity to short-duration transients such as nearby short gamma-ray bursts. With 1000 stations, BEACON achieves a sensitivity to long-duration transients, as well as the cosmogenic flux, ten times greater than existing experiments at 1 EeV. With an efficient design optimized for ultrahigh energy neutrinos, BEACON is capable of discovering the sources of neutrinos at the highest energies.

Foundation Models are neural networks that are capable of simultaneously solving many problems. Large Language Foundation Models like ChatGPT have revolutionized many aspects of daily life, but their impact for science is not yet clear. In this paper, we use a new Foundation Model for hadronic jets to solve three key challenges in collider physics. In particular, we show how experiments can (1) save significant computing power when developing reconstruction algorithms, (2) perform a complete uncertainty quantification for high-dimensional measurements, and (3) search for new physics with model agnostic methods using low-level inputs. In each case, there are significant computational or methodological challenges with current methods that limit the science potential of deep learning algorithms. By solving each problem, we take jet Foundation Models beyond proof-of-principle studies and into the toolkit of practitioners.

Mu3e is an experiment currently under construction at the Paul Scherrer Institute in Switzerland, designed to search for the Lepton Flavor Violating (LFV) decay mu^+ rightarrow e^+e^-e^+. In extensions of the Standard Model (SM) that account for neutrino masses, this decay is theoretically allowed but occurs only through extremely rare loop processes, with a predicted branching ratio of approximately O(10^{-54}). Such a small probability implies that any observation of this decay would provide clear evidence for physics beyond the SM. The Mu3e experiment aims to probe the mu^+ rightarrow e^+e^-e^+ decay with a sensitivity of approximately O(10^{-15}) in its Phase-1 and plans to achieve a sensitivity of O(10^{-16}) after future upgrades. To reach its Phase-1 ambitious goals, Mu3e is going to use the most intense continuous muon beam in the world, generating 10^{8} muon stops per second in the target placed at the center of the Mu3e. Mu3e will use three main technologies for particle detection. The tracking will done through ultra-thin (50 - 70 mu m) pixel detectors based on MuPix11 sensors. These are high-voltage monolithic active pixel sensors (HV-MAPS) with a sim 23~mum spatial resolution. The timing will be done through scintillating fibres (sim 250 ps) and tiles (sim 40 ps), coupled to silicon photomultipliers and read out by MuTRiG3 ASICs. A triggerless DAQ system based on FPGAs will collect data from the detectors, which will then undergo reconstruction in a GPU filter farm. The assembly of the detectors has started, with a detector commissioning beam time planned for 2025. This document reports on the status of the construction, installation, and data-taking plans for the near future.

In this work, we show that, if axion-like particles (ALPs) from core-collapse supernovae (SNe) couple to protons, they would produce very characteristic signatures in neutrino water Cherenkov detectors through their scattering off free protons via a , p rightarrow p , gamma interactions. Specifically, sub-MeV ALPs would generate photons with energies sim 30 MeV, which could be observed by Super-Kamiokande and Hyper-Kamiokande as a delayed signal after a future detection of SN neutrinos. We apply this to a hypothetical neighbouring SN (at a maximum distance of 100 kpc) and demonstrate that the region in the parameter space with ALP masses between 10^{-4} MeV and 1 MeV and ALP-proton couplings in the range 3 times 10^{-6}-4 times 10^{-5} could be probed. We argue that this new signature, combined with the one expected at sim 7 MeV from oxygen de-excitation, would allow us to disentangle ALP-neutron and ALP-proton couplings.

Radio detection is now an established technique for the study of ultra-high-energy (UHE) cosmic rays with energies above sim10^{17} eV. The next-generation of radio experiments aims to extend this technique to the observation of UHE earth-skimming neutrinos, which requires the detection of very inclined extensive air showers (EAS). In this article we present a new reconstruction method for the arrival direction and the energy of EAS. It combines a point-source-like description of the radio wavefront with a phenomenological model: the Angular Distribution Function (ADF). The ADF describes the angular distribution of the radio signal amplitude in the 50-200 MHz frequency range, with a particular focus on the Cherenkov angle, a crucial feature of the radio amplitude pattern. The method is applicable to showers with zenith angles larger than 60^circ, and in principle up to neutrino-induced showers with up-going trajectories. It is tested here on a simulated data set of EAS induced by cosmic rays. A resolution better than 4 arc-minutes (0.07^circ) is achieved on arrival direction, as well as an intrinsic resolution of 5% on the electromagnetic energy, and around 15% on the primary energy.

We use new cosmic microwave background (CMB) primary temperature and polarization anisotropy measurements from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) to test foundational assumptions of the standard cosmological model and set constraints on extensions to it. We derive constraints from the ACT DR6 power spectra alone, as well as in combination with legacy data from Planck. To break geometric degeneracies, we include ACT and Planck CMB lensing data and baryon acoustic oscillation data from DESI Year-1, and further add supernovae measurements from Pantheon+ for models that affect the late-time expansion history. We verify the near-scale-invariance (running of the spectral index d n_s/dln k = 0.0062 pm 0.0052) and adiabaticity of the primordial perturbations. Neutrino properties are consistent with Standard Model predictions: we find no evidence for new light, relativistic species that are free-streaming (N_{rm eff} = 2.86 pm 0.13, which combined with external BBN data becomes N_{rm eff} = 2.89 pm 0.11), for non-zero neutrino masses (sum m_nu < 0.082 eV at 95% CL), or for neutrino self-interactions. We also find no evidence for self-interacting dark radiation (N_{rm idr} < 0.134), early-universe variation of fundamental constants, early dark energy, primordial magnetic fields, or modified recombination. Our data are consistent with standard BBN, the FIRAS-inferred CMB temperature, a dark matter component that is collisionless and with only a small fraction allowed as axion-like particles, a cosmological constant, and the late-time growth rate predicted by general relativity. We find no statistically significant preference for a departure from the baseline LambdaCDM model. In general, models introduced to increase the Hubble constant or to decrease the amplitude of density fluctuations inferred from the primary CMB are not favored by our data.

This paper will discuss the microphysical simulation of interactions in liquid xenon, the active detector medium in many leading rare-event searches for new physics, and describe experimental observables useful for understanding detector performance. The scintillation and ionization yield distributions for signal and background will be presented using the Noble Element Simulation Technique (NEST), which is a toolkit based on experimental data and simple, empirical formulae, which mimic previous microphysics modeling, but are guided by data. The NEST models for light and charge production as a function of the particle type, energy, and electric field will be reviewed, as well as models for energy resolution and final pulse areas. NEST will be compared to other models or sets of models, and vetted against real data, with several specific examples pulled from XENON, ZEPLIN, LUX, LZ, PandaX, and table-top experiments used for calibrations.

We present a data acquisition~(DAQ) software based on the MIDAS framework, specifically for gaseous detectors to support the detector deployments and applications. It implements a comprehensive suite of functions, including parameter configuration, data acquisition, decoding, and storage, alongside web-based operation and real-time monitoring capabilities. We establish a fully unified workflow spanning data acquisition to offline analysis, enabling real-time visualization of signal waveforms and energy spectra. The system has been successfully deployed in the PandaX-III experiment, which utilized a high-pressure gaseous detector to search for neutrinoless double beta decay. Its performance and stability have been validated through tests involving two distinct electronics setups and joint commissioning with the detector.

Tidal disruption events (TDEs), characterized by their luminous transients and high-velocity outflows, have emerged as plausible sources of high-energy neutrinos contributing to the diffuse neutrino. In this study, we calculate the contribution of TDEs to the diffuse neutrino by employing the outflow-cloud model within the TDE framework. Our analysis indicates that the contribution of TDEs becomes negligible when the redshift Z exceeds 2. Employing a set of fiducial values, which includes outflow energy E_{rm kin}=10^{51} erg, a proton spectrum cutoff energy E_{rm p,max}=100 PeV, a volume TDE rate N=8 times 10^{-7} rm Mpc^{-3} year^{-1}, covering fraction of clouds C_V=0.1, energy conversion efficiency in the shock eta =0.1, and a proton spectrum index Gamma=-1.7, we find that TDEs can account for approximately 80\% of the contribution at energies around 0.3 PeV. Additionally, TDEs still contribute around 18\% to the IceCube data below 0.1 PeV and the total contribution is sim 24^{+2}_{-15}%. In addition, we also discuss the potential influence of various parameter values on the results in detail. With the IceCube data, we impose constraints on the combination of the physical parameters, i.e., C_{f}=NE_{rm kin}C_{rm v}eta. Future observations or theoretical considerations would fix some physical parameters, which will help to constrain some individual parameters of TDEs.

The IceCube realtime alert system has been operating since 2016. It provides prompt alerts on high-energy neutrino events to the astroparticle physics community. The localization regions for the incoming direction of neutrinos are published through NASA's Gamma-ray Coordinate Network (GCN). The IceCube realtime system consists of infrastructure dedicated to the selection of alert events, the reconstruction of their topology and arrival direction, the calculation of directional uncertainty contours and the distribution of the event information through public alert networks. Using a message-based workflow management system, a dedicated software (SkyDriver) provides a representational state transfer (REST) interface to parallelized reconstruction algorithms. In this contribution, we outline the improvements of the internal infrastructure of the IceCube realtime system that aims to streamline the internal handling of neutrino events, their distribution to the SkyDriver interface, the collection of the reconstruction results as well as their conversion into human- and machine-readable alerts to be publicly distributed through different alert networks. An approach for the long-term storage and cataloging of alert events according to findability, accessibility, interoperability and reusability (FAIR) principles is outlined.

In this article, we claim that axion-like particles (ALPs) with MeV masses can be produced with semi-relativistic velocities in core-collapse supernovae (SNe), generating a diffuse galactic flux. We show that these ALPs can be detected in neutrino water Cherenkov detectors via a , p rightarrow p , gamma interactions. Using Super-Kamiokande data, we derive new constraints on the ALP parameter space, excluding a region spanning more than one order of magnitude in the ALP-proton coupling above cooling bounds for ALP masses in the range of 1-80 MeV and ALP-proton couplings between 6times10^{-6}-2times10^{-4}. We show that the future Hyper-Kamiokande will be able to probe couplings as small as 2times10^{-6}, fully closing the allowed region above SN 1987A cooling bounds.

We present Monte Carlo (MC) simulation results from a study of a compact plastic-scintillator detector suitable for imaging fast neutrons in the 1 -- 10 MeV energy range: the miniTimeCube (mTC). Originally designed for antineutrino detection, the mTC consists of 24 MultiChannel Plate (MCP) photodetectors surrounding a 13 cm cube of boron-doped plastic scintillator. Our simulation results show that waveform digitization of 1536 optically sensitive channels surrounding the scintillator should allow for spatiotemporal determination of individual neutron-proton scatters in the detector volume to thicksim100 picoseconds and thicksim5 mm. A Bayesian estimation framework is presented for multiple-scatter reconstruction, and is used to estimate the incoming direction and energy of simulated individual neutrons. Finally, we show how populations of reconstructed neutrons can be used to estimate the direction and energy spectrum of nearby simulated neutron sources.

Coincident multimessenger observations of cosmic sources can offer numerous benefits, especially when used in the context of synergistic astrophysics. One significant advantage is enhancing the detection significance of separate detectors by correlating their data and assuming joint emission. We have formulated an approach for updating the Bayesian posterior probability of an astrophysical origin, namely p_{rm astro}, relying on multimessenger coincidences assuming an emission model. The description is applicable to any combination of messengers. We demonstrated the formalism for the gravitational waves and high-energy neutrinos case. Applying our method to the public data of candidate coincident high-energy neutrinos with subthreshold gravitational-wave triggers, we found that in the case of highly energetic neutrino coincidences, p_{rm astro} can increase from approximately sim 0.1 to sim 0.9. The amount of improvement depends on the assumed joint emission model. If models are trusted, the marked improvement makes subthreshold detections much more confident. Moreover, the model dependency can also be used to test the consistency of different models. This work is a crucial step toward the goal of uniting all detectors on equal footing into a statistically integrated, Earth-sized observatory for comprehensive multimessenger astrophysics.

In recent years, graph neural networks (GNNs) have shown tremendous promise in solving problems in high energy physics, materials science, and fluid dynamics. In this work, we introduce a new application for GNNs in the physical sciences: instrumentation design. As a case study, we apply GNNs to simulate models of the Laser Interferometer Gravitational-Wave Observatory (LIGO) and show that they are capable of accurately capturing the complex optical physics at play, while achieving runtimes 815 times faster than state of the art simulation packages. We discuss the unique challenges this problem provides for machine learning models. In addition, we provide a dataset of high-fidelity optical physics simulations for three interferometer topologies, which can be used as a benchmarking suite for future work in this direction.

In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use this kind of neural network.

We here propose a machine learning approach for monitoring particle detectors in real-time. The goal is to assess the compatibility of incoming experimental data with a reference dataset, characterising the data behaviour under normal circumstances, via a likelihood-ratio hypothesis test. The model is based on a modern implementation of kernel methods, nonparametric algorithms that can learn any continuous function given enough data. The resulting approach is efficient and agnostic to the type of anomaly that may be present in the data. Our study demonstrates the effectiveness of this strategy on multivariate data from drift tube chamber muon detectors.

The decay of metastable 'false vacuum' states via bubble nucleation plays a crucial role in many cosmological scenarios. Cold-atom analog experiments will soon provide the first empirical probes of this process, with potentially far-reaching implications for early-Universe cosmology and high-energy physics. However, an inevitable difference between these analog systems and the early Universe is that the former have a boundary. We show, using a combination of Euclidean calculations and real-time lattice simulations, that these boundaries generically cause rapid bubble nucleation on the edge of the experiment, obscuring the bulk nucleation that is relevant for cosmology. We demonstrate that implementing a high-density 'trench' region at the boundary completely eliminates this problem, and recovers the desired cosmological behavior. Our findings are relevant for ongoing efforts to probe vacuum decay in the laboratory, providing a practical solution to a key experimental obstacle.

Any observation of charged lepton flavor violation (CLFV) implies the existence of new physics beyond the SM in charged lepton sector. CLFV interactions may also contribute to the muon magnetic moment and explain the discrepancy between the SM prediction and the recent muon g-2 precision measurement at Fermilab. We consider the most general SM gauge invariant Lagrangian of Delta L=0 bileptons with CLFV couplings and investigate the interplay of low-energy precision experiments and colliders in light of the muon magnetic moment anomaly. We go beyond previous work by demonstrating the sensitivity of the LHC, the MACE experiment, a proposed muonium-antimuonium conversion experiment, and a muon collider. Currently-available LHC data is already able to probe unexplored parameter space via the CLFV process pptogamma^*/Z^*to ell_1^pm ell_1^pm ell_2^mp ell_2^mp.

We introduce a benchmark to evaluate the capability of AI to solve problems in theoretical physics, focusing on high-energy theory and cosmology. The first iteration of our benchmark consists of 57 problems of varying difficulty, from undergraduate to research level. These problems are novel in the sense that they do not come from public problem collections. We evaluate our data set on various open and closed language models, including o3-mini, o1, DeepSeek-R1, GPT-4o and versions of Llama and Qwen. While we find impressive progress in model performance with the most recent models, our research-level difficulty problems are mostly unsolved. We address challenges of auto-verifiability and grading, and discuss common failure modes. While currently state-of-the art models are still of limited use for researchers, our results show that AI assisted theoretical physics research may become possible in the near future. We discuss the main obstacles towards this goal and possible strategies to overcome them. The public problems and solutions, results for various models, and updates to the data set and score distribution, are available on the website of the dataset tpbench.org.

We reconstruct the extra-galactic gamma-ray source-count distribution, or dN/dS, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional sky-maps, which are built by varying parameters of underlying source-counts models and incorporate the Fermi-LAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold. We perform our analysis using 14 years of data collected in the (1,10) GeV energy range. The results we obtain show a source count distribution which, in the resolved regime, is in excellent agreement with the one derived from catalogued sources, and then extends as dN/dS sim S^{-2} in the unresolved regime, down to fluxes of 5 cdot 10^{-12} cm^{-2} s^{-1}. The neural network architecture and the devised methodology have the flexibility to enable future analyses to study the energy dependence of the source-count distribution.

The study of CP violation in hyperon transitions has a long history. In the early 2000s the HyperCP experiment made a major effort to seek CP-odd signals in the decay sequence Xi^-toLambda pi^- and Lambdato ppi^-, which motivated more searches. Most recently the BESIII and LHCb Collaborations have acquired or improved the upper bounds on CP violation in a variety of hyperon nonleptonic processes, including Sigma^+to npi^+ and Sigma^+to ppi^0. These measurements have not reached the standard-model level yet, but have stimulated a renewed interest in CP-violating new physics in strange-quark decay beyond what is constrained by the parameters varepsilon and varepsilon^prime from the kaon sector. In this paper, after updating the standard-model expectations for CP-odd observables in the modes Sigma^pmto Npi, we revisit new-physics scenarios that could enhance the corresponding quantities in Lambdato Npi and XitoLambdapi and apply them to the Sigma^pm modes. We find that the CP asymmetries in the latter can be significantly increased over the standard-model expectations, at levels which may be tested in the ongoing BESIII experiment and in future endeavors such as PANDA and the Super Tau Charm Facility.

New light particles have received considerable attention in recent years. Baryon-number-violating (BNV) nucleon decays involving such light particles are able to provide stringent constraints. They exhibit distinctive experimental signatures that merit thorough investigation. We systematically investigate BNV nucleon decay with a light scalar in an effective field theory framework. Within this framework, we set stringent bounds on BNV operators using available experimental data and predict the occurrence of several BNV three-body nucleon decays. We further study contributions to dinucleon to dilepton transitions in a nucleus mediated by the scalar, which complements single nucleon decay. Finally, we provide three ultraviolet-complete models that can generate different subsets of BNV operators in leading order. Our theoretical framework will facilitate experimental searches for those exotic nucleon decays.

We model a compact black hole-accretion disk system in the collapsar scenario with full transport, frequency dependent, general relativistic radiation magnetohydrodynamics. We examine whether or not winds from a collapsar disk can undergo rapid neutron capture (r-process) nucleosynthesis and significantly contribute to solar r-process abundances. We find the inclusion of accurate transport has significant effects on outflows, raising the electron fraction above Y_{rm e} sim 0.3 and preventing third peak r-process material from being synthesized. We analyze the time-evolution of neutrino processes and electron fraction in the disk and present a simple one-dimensional model for the vertical structure that emerges. We compare our simulation to semi-analytic expectations and argue that accurate neutrino transport and realistic initial and boundary conditions are required to capture the dynamics and nucleosynthetic outcome of a collapsar.

We show, for the first time, radio measurements of the depth of shower maximum (X_max) of air showers induced by cosmic rays that are compared to measurements of the established fluorescence method at the same location. Using measurements at the Pierre Auger Observatory we show full compatibility between our radio and the previously published fluorescence data set, and between a subset of air showers observed simultaneously with both radio and fluorescence techniques, a measurement setup unique to the Pierre Auger Observatory. Furthermore, we show radio X_max resolution as a function of energy and demonstrate the ability to make competitive high-resolution X_max measurements with even a sparse radio array. With this, we show that the radio technique is capable of cosmic-ray mass composition studies, both at Auger and at other experiments.

This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) by using the hls4ml tool. Given the demonstrated effectiveness of transformer models in addressing a wide range of problems, their application in experimental triggers within particle physics becomes a subject of significant interest. In this work, we have implemented critical components of a transformer model, such as multi-head attention and softmax layers. To evaluate the effectiveness of our implementation, we have focused on a particle physics jet flavor tagging problem, employing a public dataset. We recorded latency under 2 mus on the Xilinx UltraScale+ FPGA, which is compatible with hardware trigger requirements at the CERN Large Hadron Collider experiments.

The rapid data surge from the high-luminosity Large Hadron Collider introduces critical computational challenges requiring novel approaches for efficient data processing in particle physics. Quantum machine learning, with its capability to leverage the extensive Hilbert space of quantum hardware, offers a promising solution. However, current quantum graph neural networks (GNNs) lack robustness to noise and are often constrained by fixed symmetry groups, limiting adaptability in complex particle interaction modeling. This paper demonstrates that replacing the Lorentz Group Equivariant Block modules in LorentzNet with a dressed quantum circuit significantly enhances performance despite using nearly 5.5 times fewer parameters. Additionally, quantum circuits effectively replace MLPs by inherently preserving symmetries, with Lorentz symmetry integration ensuring robust handling of relativistic invariance. Our Lorentz-Equivariant Quantum Graph Neural Network (Lorentz-EQGNN) achieved 74.00% test accuracy and an AUC of 87.38% on the Quark-Gluon jet tagging dataset, outperforming the classical and quantum GNNs with a reduced architecture using only 4 qubits. On the Electron-Photon dataset, Lorentz-EQGNN reached 67.00% test accuracy and an AUC of 68.20%, demonstrating competitive results with just 800 training samples. Evaluation of our model on generic MNIST and FashionMNIST datasets confirmed Lorentz-EQGNN's efficiency, achieving 88.10% and 74.80% test accuracy, respectively. Ablation studies validated the impact of quantum components on performance, with notable improvements in background rejection rates over classical counterparts. These results highlight Lorentz-EQGNN's potential for immediate applications in noise-resilient jet tagging, event classification, and broader data-scarce HEP tasks.

Cosmological data probe massive neutrinos via their effects on the geometry of the Universe and the growth of structure, both of which are degenerate with the late-time expansion history. We clarify the nature of these degeneracies and the individual roles of both probes in neutrino mass inference. Geometry is strongly sensitive to neutrino masses: within LambdaCDM, the primary cosmic microwave background anisotropies alone impose that the matter fraction Omega_m must increase fivefold with increasing neutrino mass. Moreover, large-scale structure observables, like weak lensing of the CMB, are dimensionless and thus depend not on the matter density (as often quoted) but in fact the matter fraction. We explore the consequential impact of this distinction on the interplay between probes of structure, low-redshift distances, and CMB anisotropies. We derive constraints on the neutrino's masses independently from their suppression of structure and impact on geometry, showing that the latter is at least as important as the former. While the Dark Energy Spectroscopic Instrument's recent baryon acoustic oscillation data place stringent bounds largely deriving from their geometric incompatibility with massive neutrinos, all recent type Ia supernova datasets drive marginal preferences for nonzero neutrino masses because they prefer substantially larger matter fractions. Recent CMB lensing data, however, neither exclude neutrinos' suppression of structure nor constrain it strongly enough to discriminate between mass hierarchies. Current data thus evince not a need for modified dynamics of neutrino perturbations or structure growth but rather an inconsistent compatibility with massive neutrinos' impact on the expansion history. We identify two of DESI's measurements that strongly influence its constraints, and we also discuss neutrino mass measurements in models that alter the sound horizon.

For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimental high energy physics. This limitation is primarily due to BPE tokenization's inefficacy with numerical data. In this paper, we propose a task-agnostic architecture, BBT-Neutron, which employs a binary tokenization method to facilitate pretraining on a mixture of textual and large-scale numerical experimental data. We demonstrate the application of BBT-Neutron to Jet Origin Identification (JoI), a critical categorization challenge in high-energy physics that distinguishes jets originating from various quarks or gluons. Our results indicate that BBT-Neutron achieves comparable performance to state-of-the-art task-specific JoI models. Furthermore, we examine the scaling behavior of BBT-Neutron's performance with increasing data volume, suggesting the potential for BBT-Neutron to serve as a foundational model for particle physics data analysis, with possible extensions to a broad spectrum of scientific computing applications for Big Science experiments, industrial manufacturing and spacial computing. The project code is available at https://github.com/supersymmetry-technologies/bbt-neutron.

Novel machine learning methods for tabular data generation are often developed on small datasets which do not match the scale required for scientific applications. We investigate a recent proposal to use XGBoost as the function approximator in diffusion and flow-matching models on tabular data, which proved to be extremely memory intensive, even on tiny datasets. In this work, we conduct a critical analysis of the existing implementation from an engineering perspective, and show that these limitations are not fundamental to the method; with better implementation it can be scaled to datasets 370x larger than previously used. Our efficient implementation also unlocks scaling models to much larger sizes which we show directly leads to improved performance on benchmark tasks. We also propose algorithmic improvements that can further benefit resource usage and model performance, including multi-output trees which are well-suited to generative modeling. Finally, we present results on large-scale scientific datasets derived from experimental particle physics as part of the Fast Calorimeter Simulation Challenge. Code is available at https://github.com/layer6ai-labs/calo-forest.

The decays of pseudoscalar mesons to real and virtual photons as well as neutrino-antineutrino pairs are considered in the framework of the dispersive method based on Anomaly Sum Rules. The contribution of singlet channel involving the new non-perturbative gluon form factor of virtual photon B(q^2) is systematically taken into account. The detailed analysis of its dependence on photon virtuality q^2 relying on the available data for meson transition fomfactors is performed. It is shown that B has quite a nontrivial structure at q^2 sim 1 GeV^2 which may be a signal of the existence of pseudoscalar glueball with a mass about 1.5-2 GeV. The calculation of the decay to νbar ν pairs leads to the compatibility with the result of Arnellos, Marciano and Parsa of 1982, when pion decay is considered neglecting the mixing effects. The account for these effects results, however, in the enhancement of pion branching ratio by a factor of 3, while that for eta decay is larger by several orders of magnitude. It is stressed, that dependence on the pair invariant mass is entirely defined by QCD and coincides with that of the meson transition form factor. The role of obtained results for the physics at HHaS detector at HIAF is discussed.

Future collider experiments require unprecedented precision in measurements of Higgs, electroweak, and flavour observables, placing stringent demands on event reconstruction. The achievable precision on Higgs couplings scales directly with the resolution on visible final state particles and their invariant masses. Current particle flow algorithms rely on detector specific clustering, limiting flexibility during detector design. Here we present an end-to-end global event reconstruction approach that maps charged particle tracks and calorimeter and muon hits directly to particle level objects. The method combines geometric algebra transformer networks with object condensation based clustering, followed by dedicated networks for particle identification and energy regression. Our approach is benchmarked on fully simulated electron positron collisions at FCC-ee using the CLD detector concept. It outperforms the state-of-the-art rule-based algorithm by 10--20\% in relative reconstruction efficiency, achieves up to two orders of magnitude reduction in fake-particle rates for charged hadrons, and improves visible energy and invariant mass resolution by 22\%. By decoupling reconstruction performance from detector-specific tuning, this framework enables rapid iteration during the detector design phase of future collider experiments.

This paper presents the observation of four-top-quark (tttt) production in proton-proton collisions at the LHC. The analysis is performed using an integrated luminosity of 140 fb^{-1} at a centre-of-mass energy of 13 TeV collected using the ATLAS detector. Events containing two leptons with the same electric charge or at least three leptons (electrons or muons) are selected. Event kinematics are used to separate signal from background through a multivariate discriminant, and dedicated control regions are used to constrain the dominant backgrounds. The observed (expected) significance of the measured tttt signal with respect to the standard model (SM) background-only hypothesis is 6.1 (4.3) standard deviations. The tttt production cross section is measured to be 22.5^{+6.6}_{-5.5} fb, consistent with the SM prediction of 12.0 pm 2.4 fb within 1.8 standard deviations. Data are also used to set limits on the three-top-quark production cross section, being an irreducible background not measured previously, and to constrain the top-Higgs Yukawa coupling and effective field theory operator coefficients that affect tttt production.

We describe in the context of the particle physics (PP) standard model (SM) `PP-SM' the understanding of the primordial properties and composition of the Universe in the temperature range 130GeV>T>20keV. The Universe evolution is described using FLRW cosmology. We present a global view on particle content across time and describe the different evolution eras using deceleration parameter q. We follow the arrow of time in the expanding and cooling Universe: After the PP-SM heavies (t, h, W, Z) diminish in abundance below Tsimeq 50GeV, the PP-SM plasma in the Universe is governed by the strongly interacting Quark-Gluon content. Once the temperature drops below Tsimeq 150MeV, quarks and gluons hadronize into strongly interacting matter particles. Rapid disappearance of baryonic antimatter completes at T_B=38.2MeV. We study the ensuing disappearance of strangeness and mesons in general. We show that the different eras defined by particle populations are barely separated from each other with abundance of muons fading out just prior to T=O(2.5)MeV, the era of emergence of the free-streaming neutrinos. We discuss the two relevant fundamental constants controlling the decoupling of neutrinos. We subsequently follow the primordial Universe as it passes through the hot dense electron-positron plasma epoch. The high density of positron antimatter disappears near T=20.3keV: Nuclear reactions occur in the presence of a highly mobile and relatively strongly interacting electron-positron plasma phase. We apply plasma theory methods to describe the strong screening effects between heavy dust particle (nucleons). We analyze the paramagnetic characteristics of the electron-positron plasma when exposed to an external primordial magnetic field.

We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network's predictor of the binary label of the input state. First we look at classifying classical data sets which consist of n-bit strings with binary labels. The input quantum state is an n-bit computational basis state corresponding to a sample string. We show how to design a circuit made from two qubit unitaries that can correctly represent the label of any Boolean function of n bits. For certain label functions the circuit is exponentially long. We introduce parameter dependent unitaries that can be adapted by supervised learning of labeled data. We study an example of real world data consisting of downsampled images of handwritten digits each of which has been labeled as one of two distinct digits. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.

In the 3-3-1 model with right-handed neutrinos, three triplets of scalars engender the correct sequence of symmetry breaking, SU(3)_C times SU(3)_L times U(1)_X rightarrow SU(3)_C times SU(2)_L times U(1)_Y rightarrow SU(3)_C times U(1)_{EM}, generating mass for all fermions, except neutrinos. Tiny neutrino masses may be achieved by adding one sextet of scalars to the original scalar content. As consequence, it emerges a very complex scalar sector, involving terms that violate lepton number explicitly, too. The main obstacle to the development of the phenomenology of such scenario is the knowledge of its spectrum of scalars since, now, there are 15 massive scalar particles on it. The proposal of this work is to do an exhaustive analysis of such scalar sector with lepton number being explicitly violated at low, electroweak and high energy scales by means of trilinear terms in the potential. The first case can be addressed analytically and, as a nice result, we have observed that the scalar content of such case is split into two categories: One belonging to the 331 energy scale and the other belonging to the EWSB energy scale, with the last recovering the well known THDM+triplet. For the other cases, the scalar sector can be addressed only numerically. Hence, we proposed a very general approach for the numerical study of the potential, avoiding simplifications that can make us reach conclusions without foundation. We show that, in the case of lepton number being explicitly violated at electroweak scale, it is possible to recover the same physics of the THDM+triplet, as the previous case. Among all the possibilities, we call the attention to one special case which generates the 3HDM+triplet scenario. For the last case, when lepton number is violated at high energy scale, the sextet become very massive and decouples from the original scalar content of the 3-3-1 model.

Particle tracking is crucial for almost all physics analysis programs at the Large Hadron Collider. Deep learning models are pervasively used in particle tracking related tasks. However, the current practice is to design and train one deep learning model for one task with supervised learning techniques. The trained models work well for tasks they are trained on but show no or little generalization capabilities. We propose to unify these models with a language model. In this paper, we present a tokenized detector representation that allows us to train a BERT model for particle tracking. The trained BERT model, namely TrackingBERT, offers latent detector module embedding that can be used for other tasks. This work represents the first step towards developing a foundational model for particle detector understanding.

In this work, we present a new approach for fast tracking on multiwire proportional chambers with neural networks. The tracking networks are developed and adapted for the first-level trigger at hadron collider experiments. We use Monte Carlo samples generated by Geant4 with a custom muon chamber, which resembles part of the thin gap chambers from the ATLAS experiment, for training and performance evaluations. The chamber has a total of seven gas gaps, where the first and last gas gaps are displaced by ~1.5 m. Each gas gap has 50 channels with a size of 18-20 mm. Two neural network models are developed and presented: a convolutional neural network and a neural network optimized for the detector configuration of this study. In the latter network, a convolution layer is provided for each of three groups formed from 2-3 gas gaps of the chamber, and the outputs are fed into multilayer perceptrons in sequence. Both networks are transformed into hardware description language and implemented in Virtex UltraScale+ FPGA. The angular resolution is 2 mrad, which is comparable to the maximum resolution of the detector estimated by the minimum chi2 method. The latency achieved by the implemented firmware is less than 100 ns, and the throughput rate is 160 MHz.

Modern cosmological surveys are delivering datasets characterized by unprecedented quality and statistical completeness; this trend is expected to continue into the future as new ground- and space-based surveys come online. In order to maximally extract cosmological information from these observations, matching theoretical predictions are needed. At low redshifts, the surveys probe the nonlinear regime of structure formation where cosmological simulations are the primary means of obtaining the required information. The computational cost of sufficiently resolved large-volume simulations makes it prohibitive to run very large ensembles. Nevertheless, precision emulators built on a tractable number of high-quality simulations can be used to build very fast prediction schemes to enable a variety of cosmological inference studies. We have recently introduced the Mira-Titan Universe simulation suite designed to construct emulators for a range of cosmological probes. The suite covers the standard six cosmological parameters {omega_m,omega_b, sigma_8, h, n_s, w_0} and, in addition, includes massive neutrinos and a dynamical dark energy equation of state, {omega_{nu}, w_a}. In this paper we present the final emulator for the matter power spectrum based on 111 cosmological simulations, each covering a (2.1Gpc)^3 volume and evolving 3200^3 particles. An additional set of 1776 lower-resolution simulations and TimeRG perturbation theory results for the power spectrum are used to cover scales straddling the linear to mildly nonlinear regimes. The emulator provides predictions at the two to three percent level of accuracy over a wide range of cosmological parameters and is publicly released as part of this paper.

We present an open-source database of superconducting quantum device designs that may be used as the starting point for customized devices. Each design can be generated programmatically using the open-source Qiskit Metal package, and simulated using finite-element electromagnetic solvers. We present a robust workflow for achieving high accuracy on design simulations. Many designs in the database are experimentally validated, showing excellent agreement between simulated and measured parameters. Our database includes a front-end interface that allows users to generate ``best-guess'' designs based on desired circuit parameters. This project lowers the barrier to entry for research groups seeking to make a new class of devices by providing them a well-characterized starting point from which to refine their designs.

We present the first simulations of core-collapse supernovae in axial symmetry with feedback from fast neutrino flavor conversion (FFC). Our schematic treatment of FFCs assumes instantaneous flavor equilibration under the constraint of lepton-number conservation individually for each flavor. Systematically varying the spatial domain where FFCs are assumed to occur, we find that they facilitate SN explosions in low-mass (9-12 solar masses) progenitors that otherwise explode with longer time delays, whereas FFCs weaken the tendency to explode of higher-mass (around 20 solar masses) progenitors.

The Ultra-Violet Optical Telescope on the Swift spacecraft has observed hundreds of supernovae, covering all major types and most subtypes. Here we introduce the Swift Optical/Ultraviolet Supernova Archive (SOUSA), which will contain all of the supernova images and photometry. We describe the observation and reduction procedures and how they impact the final data. We show photometry from well-observed examples of most supernova classes, whose absolute magnitudes and colors may be used to infer supernova types in the absence of a spectrum. A full understanding of the variety within classes and a robust photometric separation of the groups requires a larger sample, which will be provided by the final archive. The data from the existing Swift supernovae are also useful for planning future observations with Swift as well as future UV observatories.

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

Results are presented from searches for the standard model Higgs boson in proton-proton collisions at sqrt(s) = 7 and 8 TeV in the Compact Muon Solenoid experiment at the LHC, using data samples corresponding to integrated luminosities of up to 5.1 inverse femtobarns at 7 TeV and 5.3 inverse femtobarns at 8 TeV. The search is performed in five decay modes: gamma gamma, ZZ, WW, tau tau, and b b-bar. An excess of events is observed above the expected background, with a local significance of 5.0 standard deviations, at a mass near 125 GeV, signalling the production of a new particle. The expected significance for a standard model Higgs boson of that mass is 5.8 standard deviations. The excess is most significant in the two decay modes with the best mass resolution, gamma gamma and ZZ; a fit to these signals gives a mass of 125.3 +/- 0.4 (stat.) +/- 0.5 (syst.) GeV. The decay to two photons indicates that the new particle is a boson with spin different from one.

We present highly sampled photometry of the supernova (SN) 2023ixf, a Type II SN in M101, beginning 2 days before its first known detection. To gather these data, we enlisted the global Unistellar Network of citizen scientists. These 252 observations from 115 telescopes show the SN's rising brightness associated with shock emergence followed by gradual decay. We measure a peak M_{V} = -18.18 pm 0.09 mag at 2023-05-25 21:37 UTC in agreement with previously published analyses.

The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.

Core collapse supernovae are among the most energetic astrophysical events in the Universe. Despite huge efforts on understanding the main ingredients triggering such explosions, we still lack of compelling evidences for the precise mechanism driving those phenomena. They are expected to produce gravitational waves due to asymmetric mass motions in the collapsing core, and emit in the meanwhile neutrinos as a result of the interactions in their high-density environment. The combination of these two cosmic messengers can provide a unique probe to study the inner engine of these processes and unveil the explosion mechanism. Among the possible detectable signature, standing accretion shock instabilities (SASI) are particularly relevant in this context as they establish a direct connection between gravitational wave emission and the outcoming neutrino flux. In this work, Hilbert-Huang transform is applied to a selected sample of 3D numerical simulations, with the aim of identifying SASI contribution and extract its instantaneous frequency. The performance of the method is evaluated in the context of Einstein Telescope.

Hypothesis ranking is a crucial component of automated scientific discovery, particularly in natural sciences where wet-lab experiments are costly and throughput-limited. Existing approaches focus on pre-experiment ranking, relying solely on large language model's internal reasoning without incorporating empirical outcomes from experiments. We introduce the task of experiment-guided ranking, which aims to prioritize candidate hypotheses based on the results of previously tested ones. However, developing such strategies is challenging due to the impracticality of repeatedly conducting real experiments in natural science domains. To address this, we propose a simulator grounded in three domain-informed assumptions, modeling hypothesis performance as a function of similarity to a known ground truth hypothesis, perturbed by noise. We curate a dataset of 124 chemistry hypotheses with experimentally reported outcomes to validate the simulator. Building on this simulator, we develop a pseudo experiment-guided ranking method that clusters hypotheses by shared functional characteristics and prioritizes candidates based on insights derived from simulated experimental feedback. Experiments show that our method outperforms pre-experiment baselines and strong ablations.

We present a web-based software tool, the Virtual Quantum Optics Laboratory (VQOL), that may be used for designing and executing realistic simulations of quantum optics experiments. A graphical user interface allows one to rapidly build and configure a variety of different optical experiments, while the runtime environment provides unique capabilities for visualization and analysis. All standard linear optical components are available as well as sources of thermal, coherent, and entangled Gaussian states. A unique aspect of VQOL is the introduction of non-Gaussian measurements using detectors modeled as deterministic devices that "click" when the amplitude of the light falls above a given threshold. We describe the underlying theoretical models and provide several illustrative examples. We find that VQOL provides a a faithful representation of many experimental quantum optics phenomena and may serve as both a useful instructional tool for students as well as a valuable research tool for practitioners.

In this work we demonstrate that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of sequential optimization or reconstruction and analysis components. We conceptually connect HEP reconstruction and analysis to modern machine learning workflows such as pretraining, finetuning, domain adaptation and high-dimensional embedding spaces and quantify the gains in the example usecase of searches of heavy resonances decaying via an intermediate di-Higgs system to four b-jets.

We study various machine learning based algorithms for performing accurate jet flavor classification on field-programmable gate arrays and demonstrate how latency and resource consumption scale with the input size and choice of algorithm. These architectures provide an initial design for models that could be used for tagging at the CERN LHC during its high-luminosity phase. The high-luminosity upgrade will lead to a five-fold increase in its instantaneous luminosity for proton-proton collisions and, in turn, higher data volume and complexity, such as the availability of jet constituents. Through quantization-aware training and efficient hardware implementations, we show that O(100) ns inference of complex architectures such as deep sets and interaction networks is feasible at a low computational resource cost.

Cryo-electron tomography (Cryo-ET) is a powerful tool in structural biology for 3D visualization of cells and biological systems at resolutions sufficient to identify individual proteins in situ. The measurements are collected by tilting the frozen specimen and exposing it to an electron beam of known dosage. As the biological samples are prone to electron damage, the samples can be exposed to only a limited dosage of electrons, leading to noisy and incomplete measurements. Thus, the reconstructions are noisy and incomplete, leading to the missing wedge problem. Currently, self-supervised learning is used to compensate for this issue. This typically involves, for each volume to recover, training a large 3D UNet on the initial noisy reconstruction, leading to large training time and memory requirements. In this work, we exploit the local nature of the forward model to train a lightweight network using only localized data from the measurements. This design provides flexibility in balancing computational and time requirements while reconstructing the volumes with high accuracy. We observe experimentally that this network can work well on unseen datasets, despite using a network trained on a few measurements.

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

WIMP-type dark matter may have additional interactions that break baryon number, leading to induced nucleon decays which are subject to direct experimental constraints from proton decay experiments. In this work, we analyze the possibility of continuous baryon destruction, deriving strong limits from the dark matter accumulating inside old neutron stars, as such a process leads to excess heat generation. We construct the simplest particle dark matter model that breaks baryon and lepton numbers separately but conserves B-L. Virtual exchange by DM particles in this model results in di-nucleon decay via nnto nbarnu and npto ne^+ processes.

Combinatorial inverse problems in high energy physics span enormous algorithmic challenges. This work presents a new deep learning driven clustering algorithm that utilizes a space-time non-local trainable graph constructor, a graph neural network, and a set transformer. The model is trained with loss functions at the graph node, edge and object level, including contrastive learning and meta-supervision. The algorithm can be applied to problems such as charged particle tracking, calorimetry, pile-up discrimination, jet physics, and beyond. We showcase the effectiveness of this cutting-edge AI approach through particle tracking simulations. The code is available online.

Using the CLEO-c detector at the Cornell Electron Storage Ring, we have measured inclusive and exclusive cross sections for the production of D+, D0 and Ds+ mesons in e+e- annihilations at thirteen center-of-mass energies between 3.97 and 4.26 GeV. Exclusive cross sections are presented for final states consisting of two charm mesons (DD, D*D, D*D*, Ds+Ds-, Ds*+Ds-, and Ds*+Ds*-) and for processes in which the charm-meson pair is accompanied by a pion. No enhancement in any final state is observed at the energy of the Y(4260).

The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which not only increases the cost of experiments but also prolongs the period of discovering new superconducting materials. In recent years, machine learning has been increasingly applied to materials science. Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials. Compared with our known similar literature, all the above algorithms reach state-of-the-art. Finally, this manuscript uses the above models to search the COD public dataset and identify 50 candidate superconducting materials with possible critical temperature greater than 90 K.

Liquid nitrogen (LN2) is a long-time favorite for physics demonstrations, with a large repertoire of crowd-pleasing experiments that are cornerstones in outreach efforts. While R1 universities usually have a ready LN2 supply for their Physics, Chemistry, and Biology departments, K-12 and two-year college teachers often have to go to specialty suppliers to obtain LN2, and also need access to expensive storage equipment. Dry ice (solid CO2) is available at many super markets, and as such its suitability as a substitute for LN2 was explored, with the results discussed below. At 77K (-196{\deg}C) LN2 is considerably colder than dry ice at 195K (-78.5{\deg}C), however some demonstrations are still viable.

Quantum Zeno Effect (QZE) has been one of the most interesting phenomena in quantum mechanics ever since its discovery in 1977 by Misra and Sudarshan [J. Math. Phys. 18, 756 (1977)]. There have been many attempts for experimental realization of the same. Here, we present the first ever simulation of QZE on IBM quantum experience platform. We simulate a two-level system for Rabi-driven oscillation and then disturb the time evolution by intermediate repetitive measurements using quantum gates to increase the survival probability of the qubit in the initial state. The circuits are designed along with the added intermediate measurements and executed on IBM quantum simulator, and the outcomes are shown to be consistent with the predictions. The increasing survival probability with the number of intermediate measurements demonstrates QZE. Furthermore, some alternative explanations for the obtained results are provided which leads to some ambiguity in giving the exact reasoning for the observed outcomes.

In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification.

The collisionless Boltzmann equation (CBE) is a fundamental equation that governs the dynamics of a broad range of astrophysical systems from space plasma to star clusters and galaxies. It is computationally expensive to integrate the CBE directly in a multi-dimensional phase space, and thus the applications to realistic astrophysical problems have been limited so far. Recently, Todorova & Steijl (2020) proposed an efficient quantum algorithm to solve the CBE with significantly reduced computational complexity. We extend the algorithm to perform quantum simulations of self-gravitating systems, incorporating the method to calculate gravity with the major Fourier modes of the density distribution extracted from the solution-encoding quantum state. Our method improves the dependency of time and space complexities on Nv , the number of grid points in each velocity coordinate, compared to the classical simulation methods. We then conduct some numerical demonstrations of our method. We first run a 1+1 dimensional test calculation of free streaming motion on 64*64 grids using 13 simulated qubits and validate our method. We then perform simulations of Jeans collapse, and compare the result with analytic and linear theory calculations. It will thus allow us to perform large-scale CBE simulations on future quantum computers.

A fully differential calculation in perturbative quantum chromodynamics is presented for the production of massive photon pairs at hadron colliders. All next-to-leading order perturbative contributions from quark-antiquark, gluon-(anti)quark, and gluon-gluon subprocesses are included, as well as all-orders resummation of initial-state gluon radiation valid at next-to-next-to-leading logarithmic accuracy. The region of phase space is specified in which the calculation is most reliable. Good agreement is demonstrated with data from the Fermilab Tevatron, and predictions are made for more detailed tests with CDF and DO data. Predictions are shown for distributions of diphoton pairs produced at the energy of the Large Hadron Collider (LHC). Distributions of the diphoton pairs from the decay of a Higgs boson are contrasted with those produced from QCD processes at the LHC, showing that enhanced sensitivity to the signal can be obtained with judicious selection of events.

Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose RS3L ("Re-simulation-based self-supervised representation learning"), a novel simulation-based SSL strategy that employs a method of re-simulation to drive data augmentation for contrastive learning in the physical sciences, particularly, in fields that rely on stochastic simulators. By intervening in the middle of the simulation process and re-running simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how RS3L pre-training enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.

We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a novel scheme to perform masked modeling based pre-training to learn permutation invariant functions on sets. More generally, this work provides a step towards building large foundation models for HEP that can be generically pre-trained with self-supervised learning and later fine-tuned for a variety of down-stream tasks. In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. We also study the fine-tuning capability of the model, showing that it can be adapted to tasks such as supervised and weakly supervised jet classification, and that the model can transfer efficiently with small fine-tuning data sets to new classes and new data domains.

X-ray observations are essential for understanding the multimessenger emission mechanisms of active galactic nuclei (AGN). Blazars, a subset of AGN whose X-ray emission predominantly originates from relativistic jets, have been proposed as promising high-energy neutrino sources. In this work, we study the candidate neutrino-emitting blazar 5BZB J0630-24064, which has been observed over multiple epochs with the XMM-Newton, NuSTAR, Neil Gehrels Swift-XRT, and eROSITA observatories. Analysis of the X-ray spectra in the 2.0-10.0 keV band shows significant variability, with high flux states adhering to a power-law model indicative of jet emission. However, during low-flux states, the spectrum reveals an additional component at hard-X-rays, indicating a transition from jet-dominated to multi-component X-ray emission, possibly associated with hadronic processes. To investigate this spectral evolution, we tested various models and found it to be consistent with corona emission or photoionised absorption processes typically observed in obscured AGN. The identification of the X-ray spectral variability in 5BZB J0630-24064, combined with its potential for neutrino production, opens new perspectives in multimessenger astrophysics of blazars highlighting the synergies between the mechanisms of the jet and the nuclear environment.

In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a variety of tasks, including those related to jets -- narrow sprays of particles produced by quarks and gluons in high energy particle collisions. This study introduces an approach to learning jet representations without hand-crafted augmentations using a jet-based joint embedding predictive architecture (J-JEPA), which aims to predict various physical targets from an informative context. As our method does not require hand-crafted augmentation like other common SSL techniques, J-JEPA avoids introducing biases that could harm downstream tasks. Since different tasks generally require invariance under different augmentations, this training without hand-crafted augmentation enables versatile applications, offering a pathway toward a cross-task foundation model. We finetune the representations learned by J-JEPA for jet tagging and benchmark them against task-specific representations.

We present a transformer architecture-based foundation model for tasks at high-energy particle colliders such as the Large Hadron Collider. We train the model to classify jets using a self-supervised strategy inspired by the Joint Embedding Predictive Architecture. We use the JetClass dataset containing 100M jets of various known particles to pre-train the model with a data-centric approach -- the model uses a fraction of the jet constituents as the context to predict the embeddings of the unseen target constituents. Our pre-trained model fares well with other datasets for standard classification benchmark tasks. We test our model on two additional downstream tasks: top tagging and differentiating light-quark jets from gluon jets. We also evaluate our model with task-specific metrics and baselines and compare it with state-of-the-art models in high-energy physics. Project site: https://hep-jepa.github.io/

This paper presents measurements of W^{pm}Z production cross sections in pp collisions at a centre-of-mass energy of 13 TeV. The data were collected in 2015 and 2016 by the ATLAS experiment at the Large Hadron Collider, and correspond to an integrated luminosity of 36.1 fb^{-1}. The W^{pm}Z candidate events are reconstructed using leptonic decay modes of the gauge bosons into electrons and muons. The measured inclusive cross section in the detector fiducial region for a single leptonic decay mode is σ_{W^pm Z rightarrow ell^{'} νell ell}^{fid.} = 63.7 pm 1.0 (stat.) pm 2.3 (syst.) pm 1.4 (lumi.) fb, reproduced by the next-to-next-to-leading-order Standard Model prediction of 61.5^{+1.4}_{-1.3} fb. Cross sections for W^+Z and W^-Z production and their ratio are presented as well as differential cross sections for several kinematic observables. An analysis of angular distributions of leptons from decays of W and Z bosons is performed for the first time in pair-produced events in hadronic collisions, and integrated helicity fractions in the detector fiducial region are measured for the W and Z bosons separately. Of particular interest, the longitudinal helicity fraction of pair-produced vector bosons is also measured.

We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of (25~h^{-1}{rm Mpc})^3 volume each: 2,184 state-of-the-art (magneto-)hydrodynamic simulations run with the AREPO and GIZMO codes, employing the same baryonic subgrid physics as the IllustrisTNG and SIMBA simulations, and 2,049 N-body simulations. The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. CAMELS contains thousands of different cosmological and astrophysical models by way of varying Omega_m, sigma_8, and four parameters controlling stellar and AGN feedback, following the evolution of more than 100 billion particles and fluid elements over a combined volume of (400~h^{-1}{rm Mpc})^3. We describe the simulations in detail and characterize the large range of conditions represented in terms of the matter power spectrum, cosmic star formation rate density, galaxy stellar mass function, halo baryon fractions, and several galaxy scaling relations. We show that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum. This emphasizes the need for marginalizing over baryonic effects to extract the maximum amount of information from cosmological surveys. We illustrate the unique potential of CAMELS using several machine learning applications, including non-linear interpolation, parameter estimation, symbolic regression, data generation with Generative Adversarial Networks (GANs), dimensionality reduction, and anomaly detection.

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the CMS experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the neural network weights, a unique data compression algorithm can be deployed for each sensor in different detector regions, and changing detector or collider conditions. To meet area, performance, and power constraints, we perform a quantization-aware training to create an optimized neural network hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework, and was processed through synthesis and physical layout flows based on a LP CMOS 65 nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates, and reports a total area of 3.6 mm^2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. This is the first radiation tolerant on-detector ASIC implementation of a neural network that has been designed for particle physics applications.

Pressure, together with temperature and magnetic field, is an important thermodynamical parameter in physics. Investigating the response of a compound or of a material to pressure allows to elucidate ground states, investigate their interplay and interactions and determine microscopic parameters. Pressure tuning is used to establish phase diagrams, study phase transitions and identify critical points. Muon spin rotation/relaxation (muSR) is now a standard technique making increasing significant contribution in condensed matter physics, material science research and other fields. In this review, we will discuss specific requirements and challenges to perform muSR experiments under pressure, introduce the high-pressure muon facility at the Paul Scherrer Institute (PSI, Switzerland) and present selected results obtained by combining the sensitivity of the muSR technique with pressure.

This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.

We present a re-discovery of G278.94+1.35 as possibly one of the largest known Galactic supernova remnants (SNR) - that we name Diprotodon. While previously established as a Galactic SNR, Diprotodon is visible in our new EMU and GLEAM radio continuum images at an angular size of 3.33x3.23 deg, much larger than previously measured. At the previously suggested distance of 2.7 kpc, this implies a diameter of 157x152 pc. This size would qualify Diprotodon as the largest known SNR and pushes our estimates of SNR sizes to the upper limits. We investigate the environment in which the SNR is located and examine various scenarios that might explain such a large and relatively bright SNR appearance. We find that Diprotodon is most likely at a much closer distance of sim1 kpc, implying its diameter is 58x56 pc and it is in the radiative evolutionary phase. We also present a new Fermi-LAT data analysis that confirms the angular extent of the SNR in gamma-rays. The origin of the high-energy emission remains somewhat puzzling, and the scenarios we explore reveal new puzzles, given this unexpected and unique observation of a seemingly evolved SNR having a hard GeV spectrum with no breaks. We explore both leptonic and hadronic scenarios, as well as the possibility that the high-energy emission arises from the leftover particle population of a historic pulsar wind nebula.

This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from the third observing run of LIGO, Virgo, and KAGRA. The search targets unmodeled transients with durations of milliseconds to a few seconds in the 30-1500 Hz frequency band, without assumptions about the incoming signal direction, polarization, or morphology. Using the Gravitational Wave Anomalous Knowledge (GWAK) method, three compact binary coalescences (CBCs) identified by existing pipelines are successfully detected, along with a range of detector glitches. The algorithm constructs a low-dimensional embedded space to capture the physical features of signals, enabling the detection of CBCs, detector glitches, and unmodeled transients. This study demonstrates GWAK's ability to enhance gravitational-wave searches beyond the limits of existing pipelines, laying the groundwork for future detection strategies.

We generalize the Hamiltonian Monte Carlo algorithm with a stack of neural network layers and evaluate its ability to sample from different topologies in a two dimensional lattice gauge theory. We demonstrate that our model is able to successfully mix between modes of different topologies, significantly reducing the computational cost required to generated independent gauge field configurations. Our implementation is available at https://github.com/saforem2/l2hmc-qcd .

We describe the design and commissioning tests for the DSA-110 Not-So-Fast Radio Burst (NSFRB) search pipeline, a 1.4 GHz image-plane single-pulse search sensitive to 134 ms-160.8 s radio bursts. Extending the pulse width range of the Fast Radio Burst (FRB) search by 3 orders of magnitude, the NSFRB search is sensitive to the recently-discovered Galactic Long Period Radio Transients (LPRTs). The NSFRB search operates in real-time, utilizing a custom GPU-accelerated search code, cerberus, implemented in Python with JAX. We summarize successful commissioning sensitivity tests with continuum sources and pulsar B0329+54, estimating the 6sigma flux (fluence) threshold to be ~290 mJy (~40 Jy ms). Future tests of recovery of longer timescale transients, e.g. CHIME J1634+44, are planned to supplement injection testing and B0329+54 observations. An offline DSA-110 NSFRB Galactic Plane Survey was conducted to search for LPRTs, covering -3.5^circ<b<5.7^circ and 141^circ<l<225^circ (~770 square degrees) in Galactic coordinates. We estimate an upper limit Poissonian burst rate ~1 hr^{-1} per square degree (~7 hr^{-1} per 3^circtimes3^circ survey grid cell) maximized across the inner |b|<0.25^circ of the surveyed region. By imposing the ~290 mJy flux limit on two representative models (the magnetar plastic flow model and the White Dwarf-M Dwarf binary model), we reject with 95% confidence the presence of White Dwarf-M Dwarf binary LPRTs with periods between ~10-70s within ~95% of the surveyed region. Combined with the prevalence of LPRTs in the Galactic Plane, our results motivate further consideration of both White Dwarf-M Dwarf binary models and isolated magnetar models. We will continue to explore novel LPRT search strategies during real-time operations, such as triggered periodicity searches and additional targeted surveys.

We introduce Nerva, a fast neural network library under development in C++. It supports sparsity by using the sparse matrix operations of Intel's Math Kernel Library (MKL), which eliminates the need for binary masks. We show that Nerva significantly decreases training time and memory usage while reaching equivalent accuracy to PyTorch. We run static sparse experiments with an MLP on CIFAR-10. On high sparsity levels like 99%, the runtime is reduced by a factor of 4times compared to a PyTorch model using masks. Similar to other popular frameworks such as PyTorch and Keras, Nerva offers a Python interface for users to work with.

We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling.

About 90% of the computing resources available to the LHCb experiment has been spent to produce simulated data samples for Run 2 of the Large Hadron Collider at CERN. The upgraded LHCb detector will be able to collect larger data samples, requiring many more simulated events to analyze the data to be collected in Run 3. Simulation is a key necessity of analysis to interpret signal, reject background and measure efficiencies. The needed simulation will far exceed the pledged resources, requiring an evolution in technologies and techniques to produce these simulated data samples. In this contribution, we discuss Lamarr, a Gaudi-based framework to speed-up the simulation production parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Deep Generative Models powered by several algorithms and strategies are employed to effectively parameterize the high-level response of the single components of the LHCb detector, encoding within neural networks the experimental errors and uncertainties introduced in the detection and reconstruction phases. Where possible, models are trained directly on real data, statistically subtracting any background components by applying appropriate reweighing procedures. Embedding Lamarr in the general LHCb Gauss Simulation framework allows to combine its execution with any of the available generators in a seamless way. The resulting software package enables a simulation process independent of the detailed simulation used to date.

Since their discovery in the late 1960's the population of known neutron stars (NSs) has grown to ~2500. The last five decades of observations have yielded many surprises and demonstrated that the observational properties of NSs are remarkably diverse. The surveys that will be performed with SKA (the Square Kilometre Array) will produce a further tenfold increase in the number of Galactic NSs known. Moreover, the SKA's broad spectral coverage, sub-arraying and multi-beaming capabilities will allow us to characterise these sources with unprecedented efficiency, in turn enabling a giant leap in the understanding of their properties. Here we review the NS population and outline our strategies for studying each of the growing number of diverse classes that are populating the "NS zoo". Some of the main scientific questions that will be addressed by the much larger statistical samples and vastly improved timing efficiency provided by SKA include: (i) the spin period and spin-down rate distributions (and thus magnetic fields) at birth, and the associated information about the SNe wherein they are formed; (ii) the radio pulsar-magnetar connection; (iii) the link between normal radio pulsars, intermittent pulsars and rotating radio transients; (iv) the slowest possible spin period for a radio pulsar (revealing the conditions at the pulsar death-line); (v) proper motions of pulsars (revealing SN kick physics); (vi) the mass distribution of NSs (vii) the fastest possible spin period for a recycled pulsar (constraining magnetosphere-accretion disc interactions, gravitational wave radiation and the equation-of-state); (viii) the origin of high eccentricity millisecond pulsars (MSPs); (ix) the formation channels for recently identified triple systems; and finally (x) how isolated MSPs are formed. We expect that the SKA will break new ground unveiling exotic systems that will challenge... [abridged]

Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world. An experimental setup that transduces data from a physical system to a stable quantum memory, and processes that data using a quantum computer, could have significant advantages over conventional experiments in which the physical system is measured and the outcomes are processed using a classical computer. We prove that, in various tasks, quantum machines can learn from exponentially fewer experiments than those required in conventional experiments. The exponential advantage holds in predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics. In some tasks, the quantum processing needed to achieve the exponential advantage can be modest; for example, one can simultaneously learn about many noncommuting observables by processing only two copies of the system. Conducting experiments with up to 40 superconducting qubits and 1300 quantum gates, we demonstrate that a substantial quantum advantage can be realized using today's relatively noisy quantum processors. Our results highlight how quantum technology can enable powerful new strategies to learn about nature.

Contribution from the USQCD Collaboration to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021).

Gravitational waves, detected a century after they were first theorized, are spacetime distortions caused by some of the most cataclysmic events in the universe, including black hole mergers and supernovae. The successful detection of these waves has been made possible by ingenious detectors designed by human experts. Beyond these successful designs, the vast space of experimental configurations remains largely unexplored, offering an exciting territory potentially rich in innovative and unconventional detection strategies. Here, we demonstrate the application of artificial intelligence (AI) to systematically explore this enormous space, revealing novel topologies for gravitational wave (GW) detectors that outperform current next-generation designs under realistic experimental constraints. Our results span a broad range of astrophysical targets, such as black hole and neutron star mergers, supernovae, and primordial GW sources. Moreover, we are able to conceptualize the initially unorthodox discovered designs, emphasizing the potential of using AI algorithms not only in discovering but also in understanding these novel topologies. We've assembled more than 50 superior solutions in a publicly available Gravitational Wave Detector Zoo which could lead to many new surprising techniques. At a bigger picture, our approach is not limited to gravitational wave detectors and can be extended to AI-driven design of experiments across diverse domains of fundamental physics.

We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-p_T jet constituents.

The mass composition of ultra-high-energy cosmic rays is an open problem in astroparticle physics. It is usually inferred from the depth of the shower maximum (Xmax) of cosmic-ray showers, which is only ambiguously determined by modern hadronic interaction models. We examine a data-driven scenario, in which we consider the expectation value of Xmax as a free parameter. We test the novel hypothesis whether the cosmic-ray data from the Pierre Auger Observatory can be interpreted in a consistent picture, under the assumption that the mass composition of cosmic rays at the highest energies is dominated by high metallicity, resulting in pure iron nuclei at energies above ~40 EeV. We investigate the implications on astrophysical observations and hadronic interactions, and we discuss the global consistency of the data assuming this heavy-metal scenario. We conclude that the data from the Pierre Auger Observatory can be interpreted consistently if the expectation values for Xmax from modern hadronic interaction models are shifted to larger values.

Construction of the Japanese second-generation gravitational-wave detector KAGRA has been started. In the next 6 \sim 7 years, we will be able to observe the space-time ripple from faraway galaxies. KAGRA is equipped with the latest advanced technologies. The entire 3-km long detector is located in the underground to be isolated from the seismic motion, the core optics are cooled down to 20 K to reduce thermal fluctuations, and quantum non-demolition techniques are used to decrease quantum noise. In this paper, we introduce the detector configuration of KAGRA; its design, strategy, and downselection of parameters.

Pulsar Wind Nebulae (PWNe) dominate the galactic gamma-ray sky at very high energies, and are major contributors to the leptonic cosmic ray flux. However, whether or not pulsars also accelerate ions to comparable energies is not yet experimentally confirmed. We aim to constrain the birth period and pair-production multiplicity for a set of pulsars. In doing so, we aim to constrain the proportion of ions in the pulsar magnetosphere and hence the proportion of ions that could enter the pulsar wind. We estimate possible ranges of the value of the average pair production multiplicity for a sample of 26 pulsars in the Australia Telescope National Facility (ATNF) catalogue, which have also been observed by the High Energy Stereoscopic System (H.E.S.S.) telescopes. We then derive lower limits for the pulsar birth periods and average pair production multiplicities for a subset of these sources where the extent of the pulsar wind nebula and surrounding supernova shell have been measured in the radio. We also derive curves for the average pair production multiplicities as a function of birth period for sources recently observed by the Large High Altitude Air Shower Observatory (LHAASO). We show that there is a potential for hadrons entering the pulsar wind for most of the H.E.S.S. and LHAASO sources we consider, dependent upon the efficiency of luminosity conversion into particles. We also present estimates of the pulsar birth period for six of these sources, which all fall into the range of simeq10-50 ms.

The Muon optimizer has received considerable attention for its strong performance in training large language models, yet the design principle behind its matrix-gradient orthogonalization remains largely elusive. In this paper, we introduce a surrogate model that not only sheds new light on the design of Muon, but more importantly leads to a new optimizer. In the same spirit as the derivation of Newton's method, the surrogate approximates the loss as a quadratic function of the perturbation to a weight matrix W using only three matrices: the gradient G, an output-space curvature matrix H, and the data matrix Z that stacks the layer inputs. By minimizing this surrogate in one step and adopting a certain isotropic assumption on the weights, we obtain the closed-form update rule (up to momentum and weight decay) W leftarrow W - ηcdot msgn(G(ZZ^top)^{-1}), where η is the learning rate and msgn(X)=UV^top if X=USV^top is a compact singular value decomposition. This new optimization method, which we refer to as Newton-Muon, shows that standard Muon can be interpreted as an implicit Newton-type method that neglects the right preconditioning induced by the input second moment. Empirically, on a reproduction of the earliest publicly released Modded-NanoGPT speedrun configuration using Muon for GPT-2 pretraining, Newton-Muon reaches the target validation loss in 6\% fewer iteration steps and reduces wall-clock training time by about 4\%.

We present final natural system optical (ugriBV) and near-infrared (YJH) photometry of 134 supernovae (SNe) with probable white dwarf progenitors that were observed in 2004-2009 as part of the first stage of the Carnegie Supernova Project (CSP-I). The sample consists of 123 Type Ia SNe, 5 Type Iax SNe, 2 super-Chandrasekhar SN candidates, 2 Type Ia SNe interacting with circumstellar matter, and 2 SN 2006bt-like events. The redshifts of the objects range from z = 0.0037 to 0.0835; the median redshift is 0.0241. For 120 (90%) of these SNe, near-infrared photometry was obtained. Average optical extinction coefficients and color terms are derived and demonstrated to be stable during the five CSP-I observing campaigns. Measurements of the CSP-I near-infrared bandpasses are also described, and near-infrared color terms are estimated through synthetic photometry of stellar atmosphere models. Optical and near-infrared magnitudes of local sequences of tertiary standard stars for each supernova are given, and a new calibration of Y-band magnitudes of the Persson et al. (1998) standards in the CSP-I natural system is presented.

Case-oriented physics education research - which seeks to refine and develop theory by linking that theory to cases - incorporates distinct practices for selecting data for analysis, generalizing results, and making causal claims. Unanswered questions about these practices may constrain researchers more familiar with the recurrence-oriented research paradigm - which seeks to inform instructional predictions by discerning reproducible, representative patterns and relationships - from participating in or critically engaging with case-oriented research. We use results from interviews with physics education researchers, a synthesis of the literature on research methodologies, and published examples of case-oriented and recurrence-oriented research to answer "hard-hitting questions" that researchers may pose. In doing so, we aim to substantiate our position that both case-oriented and recurrence- oriented PER are rigorous but that the rigor is of a different nature in each paradigm.

We propose a quantum version of a generative diffusion model. In this algorithm, artificial neural networks are replaced with parameterized quantum circuits, in order to directly generate quantum states. We present both a full quantum and a latent quantum version of the algorithm; we also present a conditioned version of these models. The models' performances have been evaluated using quantitative metrics complemented by qualitative assessments. An implementation of a simplified version of the algorithm has been executed on real NISQ quantum hardware.

Using two benchmark models containing extended scalar sectors beyond the Standard Model, we study deep learning techniques to enhance the sensitivity of resonant triple Higgs boson searches in the fully hadronic 6b channel, which suffers from the combinatorial challenge of reconstructing the Higgs bosons correctly from the multiple b-jets. More specifically, we employ the framework of Symmetry Preserving Attention Network (Spa-Net), which takes into account the permutational symmetry when a correct pairing of b-jets is achieved, to tackle both jet pairing and event classification. Significantly improved efficiency is achieved in signal and background discrimination. When comparing with the conventional Dense Neural Networks, Spa-Net results in up to 40\% more stringent limits on resonant production cross-sections. These results highlight the potential of using advanced machine learning techniques to significantly improve the sensitivity of triple Higgs boson searches in the fully hadronic channel.

The trouble in detecting low mass dark matter due to its low kinetic energy can be ameliorated in the boosted dark matter framework, where a sub-population of galactic dark matter attains very high energy after being up-scattered by energetic standard model particles. However, in such a scenario the upper limits on the cross-section obtained hitherto are typically large. Hence in the minimal extension of standard model where new mediators act as a portal between the dark and visible sectors, the direct detection limits for sub-GeV dark matter might lie within the exclusion region of other ground based searches of the mediator. To evade this deadlock, we allude to blazar boosted dark matter electron scattering in multi-ton neutrino detector Super kamiokande. We consider minimal models such as axion like particle (ALP) and vector portal dark matter being upscattered by high energy blazar jet and analyse the interesting parameter reaches from Super kamiokande in the parameter space of the mediator, surpassing the existing constraints. Besides, this scenario exhibits stronger limits for previously unexplored ALP mediated sub-MeV dark matter search which is difficult due to associated momentum suppression.

We present a low-cost ultraviolet to infrared absolute quantum efficiency detector characterization system developed using commercial off-the-shelf components. The key components of the experiment include a light source,a regulated power supply, a monochromator, an integrating sphere, and a calibrated photodiode. We provide a step-by-step procedure to construct the photon and quantum efficiency transfer curves of imaging sensors. We present results for the GSENSE 2020 BSI CMOS sensor and the Sony IMX 455 BSI CMOS sensor. As a reference for similar characterizations, we provide a list of parts and associated costs along with images of our setup.

Evaluations are critical for understanding the capabilities of large language models (LLMs). Fundamentally, evaluations are experiments; but the literature on evaluations has largely ignored the literature from other sciences on experiment analysis and planning. This article shows researchers with some training in statistics how to think about and analyze data from language model evaluations. Conceptualizing evaluation questions as having been drawn from an unseen super-population, we present formulas for analyzing evaluation data, measuring differences between two models, and planning an evaluation experiment. We make a number of specific recommendations for running language model evaluations and reporting experiment results in a way that minimizes statistical noise and maximizes informativeness.

This paper focuses on the construction of a general parametric model that can be implemented executing multiple swap tests over few qubits and applying a suitable measurement protocol. The model turns out to be equivalent to a two-layer feedforward neural network which can be realized combining small quantum modules. The advantages and the perspectives of the proposed quantum method are discussed.