1403 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 study investigates how global rotation in ultra-relativistic heavy-ion collisions modifies chemical freeze-out parameters within the hadron resonance gas model, revealing that rotation systematically lowers freeze-out temperatures and suggesting that hadron yield ratios are more sensitive indicators of these rotational effects than conventional cumulant ratios.
This paper reports recent advances in understanding liquid argon properties and position reconstruction, along with hardware upgrades to mitigate alpha backgrounds in the DEAP-3600 experiment, while outlining the design and background mitigation strategies for the upcoming 300-tonne ARGO detector.
This study quantitatively assesses the contribution of coherent photon-nuclear interactions, driven by strong electromagnetic fields in relativistic heavy-ion collisions, as a distinct background source that mimics chiral magnetic effect signals to improve the precision of separating genuine CME signals from background noise.
This paper presents the design, construction, and beam test results of the Muon Trigger Detector (MTD) for the PSI muEDM experiment, demonstrating through experimental data and Geant4 simulations that the system successfully identifies storable muons to enable a sensitivity improvement of over three orders of magnitude in the muon electric dipole moment measurement.
This review article examines the critical role of precision timing in high-energy physics, covering the underlying physical principles, key detector technologies like LGADs and MRPCs, their integration in current experiments, and emerging directions for future colliders such as the HL-LHC.
This paper presents a comprehensive theoretical calculation of the magnetic moments for open heavy-flavor ( and ) molecular pentaquark octets within a constituent quark model, revealing distinct electromagnetic signatures that differentiate between symmetric and antisymmetric light-diquark configurations and provide crucial benchmarks for identifying these states in future experiments.
This paper proposes three deep learning models—a gated spatiotemporal graph neural network and two Transformer-based architectures—that effectively tag and suppress C photon hits in liquid scintillator detectors, significantly improving energy resolution for overlapping and C events while maintaining a low misidentification rate.
This paper calculates the central charged particle multiplicity distribution using a multi-channel eikonal model with AGK cutting rules, compares the results with ATLAS data at 7 and 13 TeV, and discusses the implications of color reconnection and string percolation.
Using 9 fb of proton-proton collision data from the LHCb experiment, this paper presents the first measurement of the CP asymmetry in decays and the most precise measurement to date for decays, with both results being consistent with CP symmetry.
Using 6.1 fb of data from the BESIII detector, this study reports the first observation of the decay with a significance of , measures its branching fraction relative to , and sets an upper limit on the intermediate process while employing a Transformer-based deep learning approach for background suppression.