2610 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 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.
This paper demonstrates a quantum-enhanced dark matter search technique using a superconducting qubit to prepare a microwave cavity in a non-classical Fock state, which stimulates signal photon emission to increase the scan rate by a factor of 2.78 and excludes kinetic mixing angles for dark photons around 5.965 GHz.
This paper proposes that relic neutrinos from the Big Bang can be detected via parametric fluorescence, a coherent scattering process in atomic or molecular systems where a massive neutrino decays into a lighter neutrino and a signal photon, offering a promising detection method with event rates potentially reaching one per year in optimized target volumes.
This paper demonstrates that uncertainties in final-state interaction modeling can distort reconstructed neutrino-energy spectra in next-generation long-baseline experiments like DUNE to an extent comparable to or exceeding oscillation parameter variations, thereby creating potential degeneracies that necessitate improved theoretical characterizations and dedicated experimental measurements for robust results.
This paper introduces NuBench, an open benchmark comprising seven large-scale simulated datasets across six detector geometries, designed to facilitate the development and comparative evaluation of deep learning-based event reconstruction methods for neutrino telescopes.