1402 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 the first constraint on time-reversal invariance violating effects in polarized neutron transmission through a transversely polarized La target by applying a density matrix formalism to existing data, yielding an upper limit of at 90% confidence level despite the data's lack of optimization for such observables.
Using LHCb data from proton-proton collisions at 5.02 TeV, this paper reports the differential and integrated cross-sections of production and demonstrates a proof-of-principle method for determining the -boson mass from these measurements.
This paper introduces GraphPT, a deep learning model based on a graph neural network U-Net architecture that represents TPC data as point clouds to significantly improve dN/dx reconstruction and particle identification performance, achieving a 10% to 20% enhancement in K/π separation power compared to traditional methods.
This paper presents the design and expected performance of the hKLM, a novel iron-scintillator sampling calorimeter for the Electron Ion Collider that integrates machine learning-driven optimization and multi-dimensional readout to achieve high-resolution identification of neutral hadrons and muons with compact dimensions and excellent timing capabilities.
This paper derives general formulas for reconstructing the flavor fractions of astrophysical neutrinos at their sources from observed data, revealing an inherent divergence in distinguishing and flavors due to - interchange symmetry in the lepton mixing matrix and demonstrating that only specific combinations of flavor fractions can be precisely extracted under exact symmetry conditions.
This paper demonstrates that future Tera-Z factories, such as FCC-ee and CEPC, can serve as both discovery and precision machines for new long-lived particles by deriving analytic estimates for event yields and sensitivity, illustrated through examples like heavy neutral leptons and axion-like particles.
This paper demonstrates that the CERN SHiP experiment can probe sequential freeze-in dark matter mediated by dark photons with masses between and $10$ GeV, predicting a specific dark charge and a narrow mixing parameter range () that survives current constraints while remaining testable via proton bremsstrahlung signals.
This paper presents a probabilistic modeling approach using parameter distributions to efficiently simulate Cherenkov light yield fluctuations from particle showers in ice and water, offering a more accurate alternative to full Monte Carlo simulations for neutrino telescope experiments.
This paper reports the measurement of production cross-sections from 400 GeV/c protons interacting in a thick molybdenum/tungsten target, finding results consistent with Monte Carlo simulations and the NA50 experiment while observing no significant enhancement from secondary production or cascade collisions within the target.
This study utilizes Monte Carlo simulations of electron-positron collisions at the FCC-ee with a center-of-mass energy of 240 GeV and an integrated luminosity of 10.8 ab to search for vector-like leptons decaying into electrons and missing transverse energy within a lepton portal dark matter framework, ultimately establishing 95% confidence level exclusion limits on the vector-like lepton mass and Yukawa coupling in the absence of a signal.