1429 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 presents the first CMS measurement of the -channel single top quark production cross section in proton-proton collisions at TeV, utilizing 302 pb of 2017 data to report combined and individual cross sections for top quarks and antiquarks that are consistent with Standard Model predictions.
Using the full MicroBooNE dataset, this paper presents a new measurement of the electron neutrino-argon differential cross section without pions in the final state, revealing good agreement with generator predictions for lepton kinematics but notable discrepancies in hadronic system modeling, particularly regarding proton angles.
This paper projects the sensitivity of paleo-detectors to various non-relativistic effective field theory dark matter-nucleus interactions, demonstrating that they offer superior or comparable discovery potential to current conventional direct detection experiments across a wide range of dark matter masses and interaction types.
This paper evaluates the feasibility of deploying AMD Versal AI Engines for fixed-latency machine learning applications in high-energy physics experiments by demonstrating the performance of vectorized Boosted Decision Trees and Convolutional Neural Networks as alternatives to traditional programmable logic.
The DEAP-3600 collaboration presents a WIMP dark matter search using 790.8 live-days of liquid argon data and the Profile Likelihood Ratio method, which sets improved exclusion limits on the spin-independent WIMP-nucleon cross section despite being limited by alpha-decay backgrounds from circulating dust particulates.
This paper introduces the CONSERVAttack, a novel adversarial method designed to expose hidden vulnerabilities and uncertainties in machine learning models used in High Energy Physics by exploiting deviations between simulation and data that evade standard physical validation checks, while also proposing strategies to mitigate these risks.
This paper proposes enhanced PICOSEC detector configurations that utilize thick reflective photocathodes on readout electrodes of various avalanche multipliers to improve robustness and performance, including operation at mbar gas pressures.
Using QCD sum rules with nonperturbative effects up to dimension twelve, this paper investigates light double-gluon hybrid mesons to determine the masses and current couplings of various , , and configurations with specific quantum numbers, providing theoretical predictions to guide future experimental searches.
Using CMS data from lead-lead and proton-proton collisions at = 5.02 TeV, this study investigates the longitudinal invariance and shape evolution of jet-induced peaks in two-particle angular correlations, revealing that the peaks broaden significantly in pseudorapidity and exhibit increased longitudinal asymmetry as collision centrality and particle pseudorapidity increase.
This paper introduces ColliderAgent, the first language-driven, multi-agent system that autonomously executes end-to-end collider phenomenology workflows—from theoretical Lagrangians to detector-level analyses and exclusion limits—by leveraging a decoupled architecture and the Magnus execution backend to enable scalable, reproducible, and package-agnostic research in high-energy physics.