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 introduces the first end-to-end differentiable optical particle detector simulator that unifies simulation, calibration, and reconstruction into a single gradient-based framework, demonstrating improved accuracy, speed, and flexibility for analyzing large-scale neutrino detectors compared to traditional methods.
This paper reports the first optical observation of negative ion drift at surface pressure in a He:CF:SF mixture using an optically read-out TPC, demonstrating multi-species ion transport and validating the feasibility of large-scale, low-diffusion optical TPCs for rare event searches.
This paper extends a single-flavon -lattice Froggatt-Nielsen framework to the lepton sector, successfully generating charged-lepton mass hierarchies and a normal-ordered neutrino spectrum while predicting a distinct two-branch correlation between the atmospheric mixing angle and the CP-violating phase , with a lower-octant solution (, ) favored by theoretical priors.
The NUCLEUS experiment characterizes the unidentified low energy excess in its sapphire detector by demonstrating that the excess rate is independent of particle background levels but decreases with slower cooling procedures, following a universal power-law decay over time.
This paper proposes that intelligent, human-supervised AI agents built on deep learning and large language models represent the next evolution of the scientific method, enabling researchers to manage unprecedented data complexity and scale discovery, as demonstrated by the Dr. Sai system in particle physics.
This paper presents a physics-informed neural network approach to extract a universal small- dipole scattering amplitude directly from global DIS and photoproduction data without rigid parametric assumptions, successfully resolving the long-standing tension between total and charm cross-section channels while providing a smooth, non-negative input for Color Glass Condensate phenomenology.
This paper demonstrates a novel "tuning-by-opening" mechanism for a NbSn superconducting microwave cavity that achieves continuous frequency tuning exceeding 1 GHz while maintaining a high quality factor suitable for dark matter searches, all without inserting elements into the resonant volume.
Using 137 fb of 13 TeV proton-proton collision data from the ATLAS detector, this study searches for long-lived charginos and -sleptons via disappearing tracks and sets 95% confidence level lower mass limits of up to 880 GeV for wino charginos and 320 GeV for -sleptons, finding no significant excess over Standard Model background predictions.
Using 140 fb⁻¹ of proton-proton collision data at 13 TeV collected by the ATLAS detector, this study searches for Higgs boson decays into pairs of low-mass pseudoscalars that subsequently decay into four tau leptons, finding no significant excess over Standard Model backgrounds and setting 95% confidence level upper limits on the branching fraction between 0.06 and 0.23 for pseudoscalar masses ranging from 15 to 60 GeV.
This paper proposes and demonstrates a constrained end-to-end differentiable framework for High-Energy Physics trigger systems that jointly optimizes all processing stages against a unified physics objective, achieving a 2-4x improvement in true-positive rates for Higgs boson pair production while maintaining interpretability and calibration constraints.