1434 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 investigates a potential mixing between a toponium bound state and an elementary field to explain the CMS production excess, finding that the Multicritical Point Principle restricts the mixing angle to be less than in a minimal model and less than within specific Two-Higgs-Doublet Model scenarios.
This paper demonstrates that uncertainties in final-state interaction modeling can distort reconstructed neutrino-energy spectra in next-generation long-baseline experiments like DUNE to an extent comparable to or exceeding oscillation parameter variations, thereby creating potential degeneracies that necessitate improved theoretical characterizations and dedicated experimental measurements for robust results.
This paper presents an ultrafast plenoptic-camera system utilizing time-resolving single-photon avalanche diode arrays to achieve high-resolution 3D particle tracking in large, unsegmented scintillators, offering a cost-effective alternative to traditional dense segmentation for applications ranging from neutrino detection to medical imaging.
This paper introduces NuBench, an open benchmark comprising seven large-scale simulated datasets across six detector geometries, designed to facilitate the development and comparative evaluation of deep learning-based event reconstruction methods for neutrino telescopes.
This paper introduces MEDIC, a simulation-driven neural network framework that leverages machine learning to automate anomaly detection and localize faulty components in particle physics detectors, serving as a foundational step toward advanced data quality monitoring systems for future collider experiments.
This paper investigates a string closepacking mechanism during hadronization that increases effective string tension to explain the LHC-observed rise in strange-hadron production with multiplicity, a model that successfully tunes to data and qualitatively describes many particle ratios despite challenges with the non-strange p/pi ratio and specific spectral shapes.
This paper demonstrates a method for identifying electrons and discriminating hadrons using Cherenkov radiation in air detected by Silicon PhotoMultipliers (SiPMs), validated by CERN test beam data and Monte Carlo simulations to show high performance across a wide momentum range.
This paper presents the precise measurement of muon fluxes in the forward pseudorapidity region of the SND@LHC experiment using 2023–2025 proton and heavy-ion collision data, reporting values that agree with Monte Carlo predictions and are dominated by systematic uncertainties.
This paper demonstrates that reconstructing spin density matrices via quantum tomography and analyzing quantum information-inspired observables for near-threshold top-antitop production at the LHC significantly enhances the sensitivity to detect subtle toponium formation effects.
This paper characterizes the performance and durability of various UV optical components, including fibers, connectors, and diffusers, under cryogenic and high-rate conditions, demonstrating their reliability for in situ calibration of photosensors in large-scale liquid argon time projection chambers like DUNE.