2129 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 that axion dark matter induces electromagnetic radiation in conductors via the chiral magnetic effect, demonstrating that existing cavity experiments can constrain the axion-electron coupling to and suggesting that replacing copper walls with carbon-based conductors could enhance sensitivity to over a wider mass range.
This paper reports high-precision measurements of charged pion multiplicities and their azimuthal modulations in semi-inclusive deep inelastic scattering off proton and deuteron targets using a 10.6 GeV electron beam at Jefferson Lab, revealing consistent transverse momentum dependencies and significant azimuthal asymmetries that will enable improved determinations of quark transverse momentum distributions.
This paper proposes a gauged benchmark model featuring a compressed dark fermion spectrum and a small mixing angle to suppress direct detection constraints, demonstrating that a novel conversion mechanism effectively generates the observed dark matter relic density while remaining consistent with current collider and cosmological limits.
This paper investigates the sensitivity of the upcoming Electron-Ion Collider to purely electrophilic axion-like particles and bosons in the GeV mass range, demonstrating that the facility can significantly extend exclusion limits on these new physics scenarios through tri-electron and photon final state analyses.
Using 138 fb of proton-proton collision data at 13 TeV collected by the CMS detector, a search for boosted Higgs bosons decaying to bottom quark pairs in association with hadronically decaying W or Z bosons yielded an observed signal strength of = 0.72 , consistent with the Standard Model expectation within uncertainties.
This paper identifies unphysical kinematic features in EvtGen simulations of semileptonic decays involving resonances caused by neglected phase-space factors and proposes a reweighting method to correct the affected hadronic invariant mass distributions.
This paper presents the most comprehensive effective field theory interpretation to date by the ATLAS Collaboration, constraining 48 Wilson coefficients through a combined fit of diverse Higgs, electroweak, and top quark measurements while finding no significant deviations from the Standard Model.
The paper introduces TAMBO, a novel neutrino telescope utilizing a unique deep valley geometry to achieve unprecedented signal-to-background discrimination in the 1-1000 PeV range, thereby enabling precise mapping of high-energy astrophysical neutrino sources.
The paper introduces SPADE, an autoregressive transformer that independently embeds and delays multi-feature tokens to leverage standard self-attention for learning intra-token correlations, achieving state-of-the-art performance in high-granularity calorimeter shower simulation.
This paper presents an enhanced White Rabbit-based clock synchronization system for the CEPC accelerator that achieves a measured end-node precision of 7.30 ps under temperature variations, significantly surpassing the required 30 ps synchronization budget through architectural improvements including a Si5345A DSPLL, reduced restart uncertainty, and reinforcement learning-based PID control.