1495 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.
Inspired by recent LHCb measurements, this paper utilizes a final-state rescattering framework to predict that the decays exhibit branching fractions around with direct CP asymmetries near 1%, whereas the channel shows negligible CP violation and branching ratios highly sensitive to the spin assignments of the states.
This paper presents a comprehensive study of the non-resonant production process at the High-Luminosity LHC within the Higgs Effective Field Theory framework, utilizing both cut-based and multivariate analysis techniques to establish the first sensitivity projections for key HEFT couplings and demonstrate the channel's unique potential to probe extended top-Higgs interactions beyond the Standard Model.
This paper presents a Bayesian neural network approach for the multi-dimensional calibration of ATLAS calorimeter topo-clusters, which outperforms standard methods while providing robust uncertainty estimates that contribute to reducing systematic errors.
This paper investigates the phenomenological implications of a Left-Right Symmetric Model extended with a double type-I seesaw mechanism, demonstrating how fully determined Dirac mass textures can enhance right-handed neutrino contributions to neutrinoless double beta decay and the charged lepton flavor violation process within parameter spaces accessible to current and future experiments.
This paper investigates the impact of nuclear effects on muon- and neutrino-tungsten deep inelastic scattering events at the LHC's FASER and FASER experiments, demonstrating that a simultaneous analysis of inclusive and charm-tagged events can test the universality of nuclear effects and reduce uncertainties in parton distribution functions.
This study establishes a comprehensive, individualized optical model for the 17,612 PMTs in the JUNO experiment by integrating mass testing and reflectance data to map photocathode and anti-reflective coating thicknesses, thereby refining detector simulations and energy response accuracy beyond previous uniform assumptions.
This paper presents an improved extraction of the effective leptonic weak mixing angle, , by incorporating complementary CMS measurements to constrain parton distribution functions, resulting in the most precise single determination of this parameter to date that is consistent with the Standard Model.
Using 140 fb of 13 TeV proton-proton collision data collected by the ATLAS detector, a model-independent search for dimuon resonances in the 35 to 75 GeV mass range employed Gaussian process regression for background modeling, found no significant excess, and established new constraints on dark-photon and dark-matter-mediator models.
This paper investigates IceCube DeepCore's capability to detect Non-Standard neutrino Interactions using a model-independent parameterization and evaluates its potential to resolve the tension between T2K and NOvA measurements of the CP-violating phase .
Using 138 fb of proton-proton collision data at 13 TeV collected by the CMS experiment, this study presents the first LHC search for heavy scalar resonances decaying into a Higgs boson and a Higgs-like scalar in the all-hadronic 4q final state, employing advanced machine learning techniques to identify boosted jets and finding no significant excess over the Standard Model background.