2092 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 evaluates the environmental sustainability of ML-based data compression by calculating the carbon-equivalent break-even point where energy savings from reduced storage offset the carbon footprint of training and inference infrastructure.
The CMS collaboration at the LHC reports the first observation of significant long-range collective flow in oxygen-oxygen and neon-neon collisions at 5.36 TeV, demonstrating that the measured flow harmonics and their ratios are sensitive to the intrinsic nuclear structures of O and Ne and provide new constraints for hydrodynamic models incorporating *ab initio* nuclear inputs.
This paper proposes that asymmetric dark matter capable of propagating into extra dimensions can trigger the collapse of neutron stars into long-lived black holes that consume the entire star, thereby imposing significantly tighter constraints on dark matter masses and extra-dimensional sizes compared to standard three-dimensional scenarios.
This paper presents machine learning techniques for jet-energy calibration and flavor tagging at LHCb, which are applied to set 95% confidence level upper limits on the inclusive production of and decays using 1.6 fb of TeV $pp$ collision data.
This paper demonstrates that pre-training a deep generative model on a diverse set of synthetic calorimeter geometries (SimpleBox) enables effective transfer to unseen detectors, significantly reducing the data requirements and improving performance compared to training from scratch or using realistic detector priors.
Using 138 fb of 13 TeV proton-proton collision data from the CMS experiment, this study constrains anomalous triple gauge boson and vector boson-quark couplings via an effective field theory approach in WW and WZ production with single-lepton final states, achieving the most stringent constraints to date on triple gauge couplings by focusing on high-energy hadronic decays reconstructed as large-radius jets.
Using the CMS detector at the LHC, this study reports the first observation of enhanced baryon-to-meson production in oxygen-oxygen collisions at 5.36 TeV, providing evidence for the formation of a quark-gluon plasma and the influence of collective radial flow and quark coalescence in small collision systems.
This paper presents the design, commissioning, and performance of new deep-learning-based heavy-flavour jet identification algorithms deployed in the CMS high-level trigger for 13.6 TeV proton-proton collisions, which significantly improved signal efficiency for key physics processes including Higgs boson production and decay.
This paper proposes that axion-like particles (ALPs) with a mass around 10 GeV, produced via Higgs decays, could explain a reported CMS diphoton excess at 13.6 GeV and predict a distinct signature of wide jets with 2-prong substructure forming a 125 GeV resonance at the LHC.
The paper introduces FAST-HEP and its Flow engine, a domain-independent system that uses compiler techniques to separate scientific workflow descriptions from their execution, thereby enabling reproducible, portable, and evolvable data analysis across high-energy physics and other scientific domains.