2585 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 novel Graph Neural Network approach to mitigate the challenges of increased beam background and irregular hadronic interactions in the Belle~II electromagnetic calorimeter by representing crystal energy depositions as sparse graphs to effectively identify and remove unwanted noise before clustering.
Using 2018 ATLAS data from ultra-peripheral Pb+Pb collisions at 5.02 TeV, this study provides the first experimental observation that nuclear parton distribution modifications vary with impact parameter, evidenced by a significant difference in jet production cross-sections between collisions with and without forward neutron emission.
This paper demonstrates that tensor perturbations generated during first-order cosmological phase transitions in a secluded dark sector can produce observable CMB B-mode signals comparable to inflationary predictions, potentially complicating the interpretation of future data but offering a way to distinguish the two scenarios through multi-scale measurements.
Using quantum-correlated - decays from 7.93 fb of data collected by the BESIII experiment, this paper presents the most precise model-independent measurement of strong-phase differences for decays, which are critical inputs for reducing uncertainties in determining the -violating angle and charm mixing parameters.
This paper investigates charged lepton flavor violating top quark flavor-changing neutral current interactions at the proposed TRISTAN collider, demonstrating that an integrated luminosity of could improve current LHC constraints on scalar, vector, and tensor four-fermion contact interactions by approximately an order of magnitude.
This paper demonstrates how the Short-Baseline Near Detector (SBND) can exploit its proximity to the neutrino beam target to sample off-axis flux variations via the SBND-PRISM technique, thereby expanding physics potential and enhancing robustness against cross-section uncertainties while providing publicly available flux data and covariance matrices.
This paper investigates how present and future muon beam dump experiments can probe the five-dimensional nature of interactions by leveraging the enhanced signals from multiple Kaluza-Klein gauge bosons and their distinct decay channels to provide direct evidence for extra dimensions while constraining parameters via the muon anomaly.
This paper investigates the sensitivity of the beam normal spin asymmetry in Bhabha scattering to beyond-the-Standard-Model mediators within the JLab polarized positron program, highlighting how the observable's unique zero crossing in the Standard Model provides a clean, background-free opportunity to significantly extend search ranges for scalar and vector new physics scenarios.
This paper proposes a minimal extension of the Leptogenesis framework that links the generation of the baryon asymmetry and asymmetric dark matter through heavy Majorana neutrino decays, offering a testable TeV-scale mechanism that connects neutrino masses to observable direct detection signals.
This paper introduces the Lund Plane to Bloch (LP2B) encoding and a corresponding Quantum Tree-Topology Network (QTTN) that maps robust jet kinematics to qubit states, achieving competitive performance in object and polarization tagging with significantly fewer parameters and reduced systematic uncertainties compared to classical deep learning models, while also being validated on real quantum hardware.