2503 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.
Using 4.5 fb⁻¹ of collision data collected by the BESIII detector, researchers performed the first search for the Cabibbo-suppressed semileptonic decay , measuring its branching fraction to be with a statistical significance of .
This paper argues that the observed non-monotonic beam-energy dependence of transverse-momentum correlations in Au+Au collisions cannot be reliably attributed to critical phenomena or used to constrain the QCD critical end point due to the absence of a controlled theoretical framework, significant non-critical contributions, and inconsistencies with other susceptibility-based measurements.
This paper proposes a cosmologically constrained inelastic dark matter model where the long-lived excited state's relic abundance is dynamically determined by early-Universe conversion processes and the Lorentz structure of axion-portal couplings, offering a viable explanation for the high-recoil LZ event through either endothermic or exothermic scattering scenarios that can be distinguished by future annual modulation measurements and collider probes.
This paper introduces NuSmear, a fast and geometry-independent simulation system built on the GENIE event generator that efficiently models energy and angular smearing for neutrino-nucleon scattering events, offering a computationally efficient alternative to full Monte Carlo simulations while maintaining predictive accuracy through rigorous validation.
This paper proposes a time-dependent mixing model involving a late phase transition that allows sterile neutrinos to serve as dark matter while simultaneously generating neutrino masses, effectively circumventing astrophysical X-ray constraints that typically rule out such scenarios in standard type-I seesaw frameworks.
This paper offers a pedagogical overview of hadronic jets and event shapes at electron-positron colliders, covering jet definitions, perturbative QCD predictions, jet substructure techniques, and key topics like flavoured jets and Higgs decays.
This paper introduces flavor flux correlators as clean perturbative observables for measuring heavy quark flavor distributions, demonstrating their unique suppression of non-perturbative corrections and reformulating the heavy quark forward-backward asymmetry as a consequence of a mixed electroweak anomaly to enable precise analytical calculations and experimental verification.
This paper proposes and evaluates a new approach for generating semi-visible jet signatures in non-resonant dark matter production, introduces an optimized matrix element to parton shower matching configuration, and establishes collider limits on the scenario through a reinterpretation of ATLAS t-channel search data.
This paper introduces a density-guided conditional neural process that effectively balances the reconstruction of sharp physical features with resistance to statistical overfitting in detector efficiency estimation, outperforming existing models and methods with significantly fewer training events on MAJORANA data.
This paper proposes a method to construct a finite, weighted reference sample that globally maximizes the likelihood for learned density ratios, demonstrating that such a small dataset can accurately reproduce expected test statistic scans and distributions typically requiring millions of simulated events in neural simulation-based inference.