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.
JetFormer is a scalable, encoder-only Transformer architecture designed for particle jet tagging that achieves high accuracy and computational efficiency across both high-precision offline analysis and ultra-low-latency FPGA-based online triggers.
This paper presents a high-precision theoretical framework for studying heavy-boson-jet substructure using energy correlators, demonstrating that the characteristic angular peaks produced by the boson mass can be calculated via Sudakov resummation and directly related to measurements.
This study investigates how non-extensive statistics and hadronic composition influence the drag, diffusion, and relaxation properties of hadrons in a thermal bath, revealing that increasing the non-extensivity parameter and mass cutoffs enhances momentum transport while decreasing spatial diffusion.
The paper proposes the Poisson Log-Normal (PoLoN) process, a non-parametric framework that utilizes Gaussian processes to model Poisson log-rates, enabling effective prediction, signal detection, and parameter extraction for discrete count data.
The paper introduces FPPDF, a new open-source tool that implements the MSHT collaboration's polynomial parameterization and Hessian error approach using NNPDF's theoretical and experimental libraries, ultimately demonstrating that the impact of moving to aNLO perturbative orders is largely independent of the chosen PDF parameterization methodology.
This paper demonstrates that if a symmetry responsible for generating light Dirac neutrino masses is spontaneously broken, it can radiatively induce the explicit breaking necessary to destabilize domain wall networks, thereby creating a predictable link between the Dirac neutrino mass scale and a detectable stochastic gravitational wave signal.
Using Quantum Fisher Information, this paper demonstrates that reactor neutrino experiments achieve higher precision because they utilize quantum coherence for parameter estimation, whereas solar neutrino experiments are limited by an inherent loss of coherence that makes the estimation of purely classical.
This paper discusses the increasing integration of machine learning techniques within the CMS experiment to enhance the reconstruction and identification of physics objects in both online and offline settings, while highlighting its critical role in preparing for the high-luminosity LHC era.
This paper evaluates the commissioning and performance of b-jet triggers within the CMS High-Level Trigger system using Run-3 data to optimize real-time heavy-flavor jet selection.
This study uses Geant4 simulations to demonstrate how the distance between the photodetector plane and the gas pocket non-monotonically affects the accuracy and resolution of S2-based XY reconstruction in a dual-phase argon TPC, providing key insights for optimizing detector geometry.