2517 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 STAR Collaboration's measurements of baryon-to-charge ratios and net-proton yields in isobar and photonuclear collisions disfavor the conventional valence quark model of baryon number conservation, providing evidence that supports the alternative baryon junction hypothesis.
This paper addresses the critical challenge of model misspecification in machine learning applications for physics, arguing that robust analysis requires an iterative cycle of complementary diagnostics and model updates, underpinned by a mindset that anticipates and accommodates unforeseen errors to distinguish between new discoveries and analytical biases.
The paper introduces ORCA, a two-stage framework combining supervised contrastive learning with autoencoders to achieve both improved sensitivity and interpretability in collider anomaly detection by mapping events into an embedding space where anomalous samples can be quantitatively attributed to known physics processes.
Using LHCb data from 2015–2018, this paper presents an improved measurement of the -violating phase and related parameters in decays, yielding results consistent with the Standard Model and refining the global average to .
Pairton is a novel, iterative graph-based framework that leverages a pairformer architecture to reconstruct short-lived particles in high-energy collisions by modeling decay relationships as a masked prediction process, achieving state-of-the-art performance on fully hadronic decays.
This paper presents the first computation of the subleading twist correction from the three-particle -meson distribution amplitude at next-to-leading order using soft-collinear effective theory, thereby completing the leading-power factorization analysis and enabling improved field-theoretic predictions for electroweak penguin decays.
This paper presents a machine learning-based b-jet tagging method using a convolutional neural network trained on jet, track, and secondary vertex information for anti- jets in 13 TeV $pp$ collisions, demonstrating superior performance over traditional tagging techniques.
This paper presents a new quantum model demonstrating that bremsstrahlung emission in the ternary fission of Cf is highly sensitive to the process's geometry and dynamics, particularly the relative motion of heavy fragments, with theoretical predictions aligning well with preliminary experimental data.
This paper presents a comprehensive study demonstrating that the FCC-ee, utilizing four IDEA detectors and combining Higgs-strahlung and vector boson fusion processes, can achieve percent-to-per-mil precision on dominant Higgs hadronic decay modes and, for the first time, establish sensitivity to the rare decay to probe the strange-quark Yukawa coupling.
This paper investigates novel same-sign multi-lepton signatures arising from heavy neutral lepton production via the heavy Higgs-strahlung process () at 3 TeV and 10 TeV muon colliders within gauged extension models.