2603 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 a method to assess theoretical uncertainties in Effective Field Theories by leveraging the invariance of physical observables under field redefinitions to validate power-counting schemes and generalize uncertainty estimation from the Standard Model to non-renormalisable interactions, demonstrated through a case study in Higgs Effective Field Theory.
This paper benchmarks three exclusive nuclear ground-state shell models implemented in the NEUT neutrino event generator against recent JSNS measurements of missing energy from a monoenergetic neutrino beam, finding that spectral function models outperform relativistic mean field models and that accounting for missing energy thresholds allows all tested nuclear models to be statistically accepted.
This paper presents two machine-learning approaches for the ATLAS Muon Spectrometer to address High-Luminosity LHC challenges: a Graph Neural Network that improves background-hit rejection and reconstruction speed by 15%, and a Vision Transformer proof-of-concept achieving 98% tracking efficiency in just 2.3 ms.
This paper demonstrates that operating the Short-Baseline Near Detector (SBND) at Fermilab in off-target or beam-dump configurations significantly suppresses neutrino-induced backgrounds, thereby substantially extending its sensitivity to probe various new physics scenarios such as light dark matter, axion-like particles, heavy neutral leptons, and meson-portal models.
This paper evaluates the LHC's potential to detect the scalar radial excitation in composite and twin Higgs models, establishing current mass bounds of 0.93–1.13 TeV from Run 2 data and projecting HL-LHC sensitivities up to 2.2 TeV, with the Higgs pair decay channel identified as the most promising discovery mode.
This paper traces the historical development of the atomic bomb and examines the profound moral dilemmas faced by scientists regarding its use against Japan, aiming to contribute to future discourse on the ethical guidance of scientific research.
Using proton-proton collision data from the ATLAS experiment at 13 and 13.6 TeV, this study reports the first measurement of quantum entanglement between spins in pairs of Z bosons produced from Higgs boson decays, providing strong evidence (4.7σ significance) for entangled massive bosons at the electroweak scale.
This paper proposes a discovery-grade framework for detecting sub-MeV dark matter by searching for correlated "satellite-line combs"—multiple weak -ray lines shifted by a common energy offset below known neutron-capture transitions—using high-resolution HPGe detectors to suppress nuclear and instrumental backgrounds.
This paper presents CaloScore v2, an improved diffusion-based model for fast calorimeter shower simulation that utilizes progressive distillation to achieve high-fidelity sample generation with a single function evaluation, thereby overcoming the computational bottlenecks of traditional diffusion processes.
This paper introduces SBUnfold, a novel generative unfolding method leveraging Schrödinger Bridges and diffusion models to combine the strengths of discriminative and generative approaches, enabling efficient, high-dimensional cross-section measurements without relying on known probability densities.