2503 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 novel modular symmetry framework that unifies Dirac neutrino mass generation with inelastic self-interacting dark matter, successfully explaining the LUX-ZEPLIN 248 keV event while predicting a stochastic gravitational wave background from the annihilation of cosmological domain walls.
This paper proposes that the 248-keV nuclear recoil event observed by the LUX-ZEPLIN experiment could be explained by light boosted dark matter scattering off xenon nuclei, offering a model-independent alternative to heavy halo dark matter interpretations that rely on uncertain high-velocity tails of the Galactic velocity distribution.
This paper demonstrates that a previously proposed inelastic scalar dark matter model within a gauged framework naturally explains the recent high-energy recoil event observed by the LUX-ZEPLIN collaboration while satisfying thermal relic constraints, offering a testable scenario for future boson searches.
The NOvA experiment addresses a discrepancy between simulated and observed low-energy neutron candidates by implementing a data-driven neutron-on-carbon model to correct Geant4's overproduction of secondary photons, thereby improving the accuracy of neutrino energy reconstruction and systematic uncertainty evaluations.
This paper investigates the progressive increase in leakage current within LHCb VELO silicon sensors caused by bulk radiation damage throughout the LHC's Run 1 and Run 2 data-taking periods.
This paper presents a Graph Neural Network-based model for the MUonE experiment that utilizes simulated test-run data to achieve significantly faster 3D track reconstruction and particle identification with efficiency and resolution comparable to classical algorithms.
Motivated by the recent LUX-ZEPLIN observation of a single 248 keV nuclear recoil event, this paper proposes an inelastic singlet-doublet fermion dark matter model extended with a scalar triplet, which naturally explains the absence of lower-energy events by suppressing elastic scattering while enabling inelastic transitions and simultaneously generating Majorana neutrino masses via the Type-II seesaw mechanism.
This paper presents Lipschitz-constrained neural networks deployed on GPUs for the LHCb Run 3 software trigger, achieving improved real-time muon and electron identification efficiency and background rejection while meeting strict throughput and memory constraints.
This paper revisits two-body charmed anti-charmed baryonic decays by incorporating contributions alongside terms, demonstrating that these CKM-suppressed amplitudes are significantly enhanced in transitions and can substantially increase decay rates for specific channels like .
Using QCD two-point sum rules, this study quantitatively analyzes how the mass and decay constant of the meson in a hot and dense medium initially increase with temperature or baryon chemical potential before reaching a maximum and eventually vanishing at critical values that shift to lower thresholds as the opposing parameter increases.