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.
The DeLLight project has experimentally validated a new "High-Frequency Phase Noise Suppression" (HFPNS) method to mitigate mechanical vibrations in an interferometer, paving the way for the picometer-scale sensitivity required to detect quantum electrodynamics-induced vacuum nonlinearity.
This paper presents a multi-modal graph neural network that improves Belle II track reconstruction efficiency and purity by simultaneously processing data from both the central drift chamber and the silicon vertex tracker to mitigate the effects of high backgrounds and detector ageing.
This paper demonstrates the first successful application of an online, charge-based data selection algorithm in a liquid argon time projection chamber using MicroBooNE data, providing a proof-of-principle for real-time signal preservation in future large-scale experiments like DUNE.
This paper provides comprehensive analytical expressions and practical approximations for one-loop new physics contributions to the muon magnetic dipole moment, offering a universal reference for evaluating the sign and magnitude of scalar and vector mediator interactions in various theoretical models.
This paper proposes that while the current configuration of the James Webb Space Telescope is insensitive to dark photon dark matter, a modified mirror design could allow future space telescopes to detect these particles during ground testing with sensitivities significantly exceeding current experimental limits.
The paper proposes two cost-effective, medium-baseline neutrino experiments—SINE (a surface scintillator detector) and UNDINE (a submerged water Cherenkov detector)—to study LHC-produced neutrinos and constrain neutrino cross sections and cosmic-ray physics.
The paper proposes "QuGrav," a method for detecting high-frequency gravitational waves by using quantum bosonic modes (qumodes) to enhance photon conversion via the inverse Gertsenshtein effect, potentially reaching sensitivities capable of exploring cosmological gravitational wave backgrounds.
This paper demonstrates that a Graph Neural Network (GNN) outperforms an XGBoost classifier in enhancing the sensitivity of searches at 13.6 TeV, significantly improving the expected upper limits on the double Higgs production cross-section and the Higgs boson self-coupling () compared to current ATLAS results.
This study estimates the production yields and event rates of high-energy neutrinos from 100 TeV proton-proton collisions at the Future Circular Collider and evaluates the feasibility of detecting direct boson production from neutrino-nucleus interactions.
This paper investigates the use of convolutional neural networks (CNNs) as binary classifiers to distinguish photon-triggered pulses from background noise in transition edge sensors for the ALPS II experiment, finding that the CNN approach failed to outperform traditional cut-based analysis and suggesting that regression or unsupervised models may be more effective for future signal processing.