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 analyzes Belle II data on -meson decays within the Standard Model Effective Field Theory framework to constrain complex, lepton-flavor-dependent Wilson coefficients, finding that while the Standard Model remains consistent with observations, certain New Physics couplings exhibit a mild preference for non-zero values.
This paper presents remage, an open-source, Python-driven Geant4 framework designed to standardize and streamline Monte Carlo simulations for low-background physics experiments by separating geometry definition from the compiled application and providing validated tools for radiogenic and cosmogenic background modeling.
This paper presents a comprehensive calculation of radiative corrections to two-neutrino and neutrinoless double- decay using heavy-nucleus effective field theory, revealing that neglecting these effects—particularly real-photon emission and a universal double-weak Sirlin function—significantly biases experimental analyses and increases the neutrinoless decay rate by approximately 5%.
This paper employs a species-resolved anisotropy-response scaling framework to demonstrate that -meson elliptic and triangular flow in heavy-ion collisions deviate from light-flavor scaling due to charm-specific attenuation, quantified by a collision-energy and centrality-dependent rescaling factor and a small hadronic-rescattering correction, thereby providing new differential benchmarks for heavy-quark transport and medium evolution.
This paper extends the Parameter Goodness-of-fit (PG) test to handle scenarios where data sets are constrained by a common prior, demonstrating that under a Gaussian approximation, the resulting test statistic follows a generalized distribution suitable for calculating p-values.
The paper introduces ACRAIC, an automated tool within the Sherpa framework that implements coherent amplitude-level QED resummation for general scattering processes, enabling precise predictions with significantly reduced perturbative uncertainties for future colliders.
This paper derives the asymptotic null distribution of the likelihood ratio statistic for bounded parameters in both scalar and two-dimensional cases, utilizing these results to construct accurate likelihood-based confidence curves and providing a moment-matching approximation for the complex bivariate distributions, with applications demonstrated in high-energy physics.
This paper introduces a new pulse shape model explaining deviations in early COSINUS prototype detectors, revealing that light absorption in the carrier crystal creates a light-enhanced phonon signal that enables particle discrimination with a single channel and informs the design of the final remote TES detector.
This thesis advances the theoretical and statistical toolkit for searching for light dark matter (MeV–GeV) at fixed-target experiments like LDMX and SHiP by developing comprehensive frameworks for model building, likelihood-based inference, global fits, and improved signal simulations that incorporate advanced nuclear modeling.
The paper introduces SParTA, a modular and efficient transformer architecture for jet tagging that achieves state-of-the-art performance by factorizing constituent processing into a quadratic attention mechanism for hard constituents and a linear cross-attention mechanism for soft constituents, thereby significantly reducing computational complexity and parameter count compared to existing models.