2131 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 Hybrid Quantum Machine Learning framework that integrates parameterized quantum circuits with classical neural networks to significantly enhance the sensitivity of double Higgs boson searches in the channel at the LHC, outperforming both state-of-the-art classical and purely quantum models in constraining production cross-sections and coupling parameters.
This paper reports the successful development and test-beam validation of a prototype LYSO-based active converter for future experiments, demonstrating a time resolution of 25 ps and a light yield of photoelectrons that significantly exceed the design requirements for detecting 52.8 MeV photons.
This paper applies quantum information theory to axion-photon and neutrino systems, demonstrating how their coupled dynamics generate entanglement, characterizing the resulting quantum correlations and speed limits, and establishing connections between axion phenomenology, neutrino oscillations, and fundamental quantum resources.
This paper presents the first-ever measurement of energy resolution in a Liquid Argon Time Projection Chamber (LArTPC) at the MeV scale, utilizing monoenergetic signals from Tl decays in the MicroBooNE detector to determine a resolution of approximately 7.52% and validate simulation predictions.
This paper investigates the projected sensitivity of the FCC-ee at GeV with 5 ab luminosity to Lepton Flavor Violating Higgs decays into , , and final states, establishing 95% CL upper limits on their branching ratios and demonstrating that FCC-ee constraints surpass low-energy searches for the and channels, while remaining less stringent for the channel.
This paper reviews the conceptual framework, potential pitfalls, and validation strategies of AI-based model-agnostic search techniques designed to enhance the discovery potential of complex scientific data by prioritizing broad exploration over specific theoretical hypotheses.
Using 140 fb of 13 TeV proton-proton collision data collected by the ATLAS detector, this paper presents a measurement of the -jet production cross-section that achieves a relative precision twice as good as previous results and is consistent with next-to-leading-order QCD predictions.
This paper demonstrates that deep-learning-based trigger algorithms, particularly a supervised neural network and an MPDR-based anomaly detection model, significantly outperform traditional hit-count triggers in identifying low-energy neutrino events for the Hyper-Kamiokande experiment while maintaining real-time feasibility with sub-millisecond GPU inference latencies.
The CJ26 global QCD analysis presents a new set of NLO parton distribution functions by incorporating the complete suite of JLab 6 GeV and the first published 12 GeV data to uniquely disentangle higher-twist effects from off-shell nucleon corrections, thereby significantly reducing uncertainties in the large- structure function and valence quark ratios.
Drawing from a May 2023 workshop, this paper calls for resource-aware research in the fields of Universe and Matter by outlining a portfolio of digital transformation measures designed to simultaneously advance scientific progress and mitigate climate change through reduced fossil fuel reliance.