2160 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 a comprehensive, web-enabled data acquisition system based on the MIDAS framework for gaseous detectors, which has been successfully deployed and validated in the PandaX-III experiment to support real-time monitoring and a unified workflow from data acquisition to offline analysis.
This paper presents a sensitivity study using the BESIII experiment to propose a model-independent method for determining the branching fraction of decays into three-body final states by simultaneously analyzing four specific semileptonic -meson decay channels.
This paper presents a self-contained treatment of Blobel's Regularized Unfolding (BRU), a method that models true distributions using cubic B-splines and employs eigenmode decomposition for automatic regularization strength determination, offering a distinct alternative to conventional histogram-based approaches like Tikhonov regularization and Richardson-Lucy iteration.
This paper argues that optimizing jet classifiers solely for high performance metrics like accuracy or AUC can lead to models with low resilience and high model dependence, advocating instead for a balanced approach that considers the trade-offs inherent in the Pareto frontier of resilient jet tagging.
The SNO+ collaboration measured a cosmogenic neutron yield in ultra-pure water at high muon energies, finding agreement with FLUKA simulations over GEANT4 and revealing a lower yield than in heavy water, which underscores the critical influence of target material composition on neutron production for future underground experiments.
This paper introduces a generalizable foundation model for calorimetry that leverages next-token transformer architectures combined with Mixture-of-Experts pre-training and parameter-efficient fine-tuning to enable modular, scalable, and computationally efficient simulation of particle showers across diverse materials and detector configurations without catastrophic forgetting.
This paper presents an indirect determination of the top-quark pole mass as GeV by performing a global analysis of parton distribution functions within the NNPDF framework, which incorporates high-precision QCD and electroweak corrections, toponium contributions, and lattice constraints on while refitting PDFs to properly correlate uncertainties across various ATLAS and CMS measurements.
This paper proposes that a future precision $ee$ Higgs factory could perform the first spacetime-resolved test of quantum nonlocality in Higgs boson decays by measuring spin correlations between spacelike-separated lepton decays, thereby enabling the exclusion of superluminal entanglement signaling theories.
This paper investigates the sensitivity of the FASER and SHiP experiments to dark photons and U(1) gauge bosons produced via dark Higgs boson decays, incorporating new single-boson production channels, deriving constraints from current FASER data, and projecting future discovery potential for these models including those with freeze-in sterile neutrino dark matter.
The paper introduces \textsc{Albert}, a neuro-symbolic AI framework that successfully rediscovered the Standard Model and autonomously predicted the top quark's mass using only legacy LEP data, demonstrating a rigorous, hallucination-free approach to discovering new physics.