2503 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 proposes that the mass ratios and mixing parameters of quarks and leptons are governed by a level-four modular symmetry () derived from a harmonic pattern in their masses, enabling a parameter-free prediction of the CKM matrix, neutrino properties, and the unitarity triangle with high precision against experimental data.
This paper proposes and validates a self-supervised transformer architecture that learns heavy-flavour decay maps from LHCb data without explicit labels, demonstrating improved tagging performance and the ability to detect decays with invisible particles by inferring masked particle information and identifying anomalies in reconstructed jets.
This paper proposes a Bell test framework for collider decay processes that, while requiring postselection which introduces a loophole, demonstrates that any local model exploiting this loophole must be nonclassical in the sense of measurement contextuality, thereby allowing the violation of a derived Bell inequality to witness either Bell nonlocality or contextuality.
This paper investigates the electromagnetic properties of vector and axial-vector heavy-light hybrid mesons using the light cone QCD sum rules framework to derive magnetic dipole and electric quadrupole form factors and moments for various bottom and charm hybrid configurations.
This paper reports on the operational performance of the PLUME detector, a dedicated Cherenkov-light-based luminosity monitor at LHCb, during the 2024–2026 Run 3 data-taking period, highlighting its role in real-time luminosity control and recording an integrated luminosity of fb at TeV.
This paper investigates whether the isolated nuclear-recoil event reported by the LUX-ZEPLIN (LZ) collaboration could originate from elastic neutrino-nucleus scattering, concluding that kinematic constraints, the recoil spectrum, and existing limits rule out this explanation across all considered Standard Model and beyond scenarios, including exotic neutrino fluxes from dark matter and primordial black holes.
This study presents a novel current-readout technique that digitizes photomultiplier output currents to enable ultra-high-rate measurements up to 1 Gcps, successfully overcoming pulse pileup and deadtime limitations in muon spin rotation experiments while paving the way for future n-adic non-binary logic circuits.
This paper reports on the GRASIAN collaboration's development and characterization of a cryogenic hydrogen beam, detailing methods to reduce background and detect slow atoms to enable the first observation of gravitational quantum states in atoms and precision spectroscopy.
This paper introduces ABCDisCoTEC, an enhanced machine learning method that improves background estimation in LHC data by directly minimizing non-closure through a differentiable loss term and utilizing the modified differential method of multipliers to ensure robust variable independence, thereby overcoming the limitations of previous ABCDisCo approaches.
This paper introduces an optimal transport-based model calibration framework that resolves simulation-data discrepancies in high-dimensional particle physics simulations, enabling the unbiased application of powerful foundation models for tasks like jet tagging at the Large Hadron Collider.