1495 papers
Hep-Ex explores the fascinating intersection where particle physics meets experimental reality. This field investigates how scientists build massive detectors and accelerate particles to test the fundamental laws of nature, turning abstract theories into measurable data. It is the rigorous process of searching for new particles or forces that could reshape our understanding of the universe, often requiring years of collaboration and engineering.
At Gist.Science, we ensure these discoveries become accessible to everyone. We process every new preprint in this category directly from arXiv, generating both plain-language explanations for curious readers and detailed technical summaries for specialists. Our goal is to bridge the gap between complex experimental results and public understanding without losing scientific nuance.
Below are the latest papers in Hep-Ex, freshly summarized and ready for you to explore.
This paper develops a revised TMD factorization formalism for dijet production with a transversely polarized target at the EIC, incorporating NLO evolution kernels to predict significant gluon Sivers asymmetries ranging from 5% to 50%.
This paper presents the design, deployment, and performance verification of the Murchison Widefield Array Particle Detector Array (MWA PDA), an eight-detector system that successfully identifies cosmic ray extensive air showers to trigger radio data capture and serves as a pathfinder for future Square Kilometre Array instruments.
This paper presents a combined analysis of solar neutrino data from the XENONnT, PandaX-4T, and LUX-ZEPLIN experiments to constrain the solar B neutrino flux and weak mixing angle while establishing competitive bounds on nonstandard neutrino interactions mediated by both light and heavy vector particles.
This paper introduces CelloAI, a suite of repeatable and automated benchmarks designed to evaluate Large Language Models on HEP/HPC-specific tasks, including code documentation, GPU kernel generation, and graphical data analysis, to address the unique constraints of scientific software development.
This paper proposes a new, information-theoretic measure of fine-tuning based on the Fisher information metric, which generalizes the Barbieri--Giudice criterion to multiple correlated parameters and provides a geometric interpretation of sensitivity as the stretching of the parameter space submanifold within observable space.
This paper demonstrates that deep learning models, including convolutional and transformer architectures, offer a promising solution for identifying challenging C pile-up events in the JUNO experiment, thereby helping to preserve the high energy resolution required for determining the neutrino mass ordering.
ImpCresst is a versatile, Geant4-based Monte Carlo simulation tool developed by the CRESST collaboration to model backgrounds and calibration signals in solid-state detectors at keV energies, featuring dynamic CAD-based geometry, advanced particle generators for radionuclides, and a workflow optimized for HPC environments.
Using 164 fb of 13.6 TeV proton-proton collision data collected by the ATLAS detector from 2022 to 2024, this study searches for massive long-lived particles decaying into displaced vertices and displaced muons, finding no significant excess over the expected background and setting 95% confidence level upper limits on the production cross-sections of various -parity-violating supersymmetry models.
This paper demonstrates that upcoming low-threshold coherent elastic neutrino-nucleus scattering experiments at major spallation sources (ESS, J-PARC, and CSNS) will significantly probe previously unexplored parameter space for sub-GeV thermal dark matter produced via neutral pion decays and mediated by vector portals.
This paper reframes Quantum Neural Network design from state reachability to learnability by introducing geometric design principles and the almost Complete Local Selectivity (aCLS) criterion, demonstrating that architectures requiring joint dependence on data and trainable weights enable adaptive feature learning and outperform traditional schemes with greater efficiency.