2160 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.
The ATLAS Collaboration introduces GN2, a novel transformer-based algorithm that significantly improves heavy-flavour jet identification performance by processing low-level tracking data end-to-end, thereby enhancing key physics analyses such as Higgs boson studies.
This study utilizes an AI-driven Jet Origin Identification framework to demonstrate that optimizing the vertex detector's inner radius and spatial resolution at the CEPC significantly enhances the precision of measuring Higgs decays to second-generation quarks, particularly .
Using 140 fb of proton-proton collision data at 13 TeV, the CMS experiment achieved the first full exclusive reconstruction of the B, B, and B mesons, measuring their mass differences with a precision improved by an order of magnitude compared to previous results.
This paper presents a fast and accurate detector simulation framework using Vision Transformers, Conditional Flow Matching, and Normalising Flows to emulate Geant4 results on CaloChallenge datasets, achieving minimal deviation from high-fidelity simulations while significantly improving generation speed.
This paper introduces the first self-supervised training pipeline for neutrino telescopes using masked point transformers to leverage real data, thereby significantly reducing reliance on simulations and mitigating associated systematic uncertainties.
This paper details the design and construction of the watertight optical modules for BUTTON-30, a neutrino detector demonstrator located in the STFC Boulby underground facility that aims to test gadolinium-loaded water-based liquid scintillator for future large-volume observatories and nuclear reactor monitoring.
This paper investigates the feasibility of extracting baryon-antibaryon generalized distribution amplitudes from the process using QCD factorization and numerical estimates, demonstrating that such a measurement is achievable at the Belle II experiment.
This paper introduces the data-driven ARGOS metric, a theoretically grounded and empirically robust tool for selecting the most sensitive anomaly detection models in a model-agnostic way, demonstrating its superiority over existing metrics like binary cross-entropy loss in tasks such as hyperparameter tuning and feature selection.
This study investigates the structural effects of , , and mesons on Be nuclei using HAL QCD potentials and the Gaussian expansion method, revealing that the meson strongly binds the system into stable states while and interactions are weaker and form only shallow bound states, with specific predictions for new -charmonium bound states and the non-Borromean nature of .
The paper introduces EveNet, a foundation model pretrained on 500 million simulated collision events that leverages a hybrid learning objective to outperform state-of-the-art methods in diverse high-energy physics tasks, demonstrate exceptional data efficiency, and successfully validate its transferability to real experimental data for precision physics and discovery.