1442 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.
Using 138 fb of 13 TeV proton-proton collision data, the CMS collaboration performed a first-of-its-kind search for dark matter production associated with bottom quarks and a lepton pair within a 2HDM+a framework, finding no evidence of new physics and setting stringent upper limits on the production cross-sections of heavy Higgs bosons and pseudoscalar mediators.
Using combined data from the Belle and Belle II experiments, researchers have observed the radiative decay for the first time with high significance and measured its branching fraction ratio relative to the mode, providing crucial new constraints on the particle's internal quark structure.
The paper introduces HadroTOPS, a flexible Monte Carlo event generator designed to simulate hadronic two-photon fusion events in electron-positron collisions with exact leading-order QED coupling and customizable phase space decays, specifically supporting partial wave analyses for various final states relevant to collaborations like BESIII.
This paper introduces KANELÉ, a framework that leverages the spline-based structure of Kolmogorov-Arnold Networks (KANs) to enable efficient, low-latency FPGA deployment via lookup tables, achieving significant speedups and resource savings while matching or surpassing existing LUT-based architectures on various benchmarks.
This paper presents the design, fabrication, and performance evaluation of a full-scale muon scattering tomography system utilizing four layers of high-precision plastic-scintillator detectors with 0.09-strip-pitch spatial resolution for non-destructive imaging of high-Z materials.
This paper proposes a solution to the long-standing gallium anomaly by revising the theoretical calculation of the neutrino capture cross-section on Ga, demonstrating that moving beyond standard leptonic wave function factorization and incorporating constrained Gamow-Teller transition densities can resolve the observed deficit without invoking new physics.
Using 138 fb of proton-proton collision data at = 13 TeV collected by the CMS detector, this paper presents the first search for the pair production of heavy spin-1/2 or spin-3/2 resonances decaying into a top quark, a photon, a top quark, and a gluon, setting 95% confidence level exclusion limits on resonance masses below 930 GeV and 1330 GeV for spin-1/2 and spin-3/2 scenarios, respectively.
This paper proposes a data-driven calibration strategy using B solar neutrinos and muon-decay-at-rest neutrinos as standard candles to constrain the poorly known electron neutrino-argon cross section, thereby significantly reducing model-dependent biases in extracting Galactic supernova spectral properties at the DUNE experiment.
This paper introduces ParticleTransformer, a fully machine-learned, multi-task approach that achieves state-of-the-art performance in identifying, classifying, and reconstructing the kinematics of hadronic tau leptons for the FCC-ee TeraZ program, significantly outperforming conventional heuristic algorithms.
This paper presents a validated framework for reconstructing and identifying electromagnetic showers in the FASERnu emulsion detector using CERN test-beam data, achieving high background rejection and efficient energy reconstruction to support electron neutrino measurements at the LHC.