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 presents a conceptual design and simulation study of a novel high-granularity crystal electromagnetic calorimeter using orthogonally arranged scintillating bars and SiPMs, demonstrating an energy resolution of that significantly exceeds the requirements for precision measurements at future Higgs factories.
This paper introduces the first end-to-end differentiable optical particle detector simulator that unifies simulation, calibration, and reconstruction into a single gradient-based framework, demonstrating improved accuracy, speed, and flexibility for analyzing large-scale neutrino detectors compared to traditional methods.
This paper demonstrates that Graph Neural Networks outperform traditional models like TabNet in estimating muon particle momentum for the CMS experiment, highlighting the critical role of node feature dimensionality and the benefits of leveraging the data's inherent graph structure to improve trigger system efficiency.
Using the largest published sample of approximately one million muon antineutrino events from the NOvA Near Detector, this study presents the first triple-differential measurement of the charged-current inclusive cross section, revealing significant energy- and angle-dependent discrepancies between the data and current neutrino event generator predictions.
This paper proposes a physics-informed Bayesian latent diffusion model that integrates probabilistic encoding, diffusion dynamics, and physics constraints to achieve stable and reliable anomaly detection for new physics searches in collider data.
This paper reports the first optical observation of negative ion drift at surface pressure in a He:CF:SF mixture using an optically read-out TPC, demonstrating multi-species ion transport and validating the feasibility of large-scale, low-diffusion optical TPCs for rare event searches.
This paper extends a single-flavon -lattice Froggatt-Nielsen framework to the lepton sector, successfully generating charged-lepton mass hierarchies and a normal-ordered neutrino spectrum while predicting a distinct two-branch correlation between the atmospheric mixing angle and the CP-violating phase , with a lower-octant solution (, ) favored by theoretical priors.
This paper summarizes how derivatives of the QCD pressure calculated via lattice QCD are used to construct observables that probe the QCD phase diagram at physical quark masses, specifically analyzing the -flavor chiral phase transition, deconfinement, and the convergence properties of Taylor expansions in the search for the critical endpoint.
The NUCLEUS experiment characterizes the unidentified low energy excess in its sapphire detector by demonstrating that the excess rate is independent of particle background levels but decreases with slower cooling procedures, following a universal power-law decay over time.
This paper proposes that intelligent, human-supervised AI agents built on deep learning and large language models represent the next evolution of the scientific method, enabling researchers to manage unprecedented data complexity and scale discovery, as demonstrated by the Dr. Sai system in particle physics.