2517 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 measurement of the polarization of and baryons in Ne collisions at GeV by the LHCb experiment, reporting a significant positive polarization for and a compatible-with-zero result for .
Using a large sample of events collected by the BESIII detector, this study reports the first observation of the decays for , providing new measurements of their branching fractions and evidence for hyperon resonance structures in the final states.
This white paper summarizes scientific challenges and AI/ML opportunities identified through the FAIRS Japan 2024 unconference, highlighting common technical themes such as high-dimensional reconstruction, fast simulation, and anomaly detection across accelerator physics, cosmology, and neutrino physics.
This paper demonstrates that incorporating precision timing information from a future HL-LHC detector layer into vector-boson-fusion Higgs decay searches significantly enhances sensitivity to long-lived particles by converting displaced vertices into four-dimensional objects, thereby suppressing prompt heavy-flavor backgrounds and extending reach into regimes where traditional spatial-only searches lose acceptance.
Using LHCb data from $pp$ collisions at 13 TeV, this paper reports the first observation of the Cabibbo-suppressed decay , measuring its branching fraction to be and determining its decay asymmetry parameter to be .
This study systematically compares classical and quantum machine learning architectures for regression on simulated high-energy physics collision data, finding that while classical models like CNNs and LSTMs currently offer marginally better performance, quantum counterparts achieve competitive accuracy with significantly fewer trainable parameters, highlighting a distinct parameter-efficiency advantage for near-term quantum devices.
This editorial outlines a research initiative aimed at advancing green computing in high-energy physics and astrophysics by evaluating current energy consumption, identifying optimization opportunities, and fostering cross-disciplinary collaboration to enhance sustainability.
This paper evaluates and compares classical and graph-neural-network-based methods for reconstructing annihilation vertices in the HIBEAM experiment, finding that while both approaches achieve comparable accuracy for vertex coordinates, machine learning offers unique advantages in extracting event-shape information for downstream analysis.
Using 138 fb of proton-proton collision data at = 13 TeV collected by the CMS detector, this study presents the first search for TeV-scale scalar leptoquarks produced via muon-quark scattering, finding no evidence for their existence and setting new exclusion limits that extend previous coverage to masses between 1.5 and 3.6 TeV for LQ(u) and above 1.8 TeV for LQ(b).
This thesis presents the first full-phase-space, high-dimensional measurement of the Z+jets differential cross section at the LHC using the ATLAS experiment, demonstrating how artificial intelligence-based unfolding techniques can fully exploit complex datasets to characterize fundamental particle interactions.