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.
This paper presents the first theoretical calculation demonstrating that a material-asymmetric Sagnac speed meter can improve quantum-noise-limited sensitivity to vector dark matter by nearly two orders of magnitude compared to a Fabry–Perot Michelson interferometer at low masses, primarily by reducing low-frequency quantum backaction.
Using 207 fb of proton-proton collision data at 13 and 13.6 TeV, the CMS collaboration performed a search for Higgs boson compositeness in high-mass dilepton final states, establishing stringent constraints on the oblique parameters and and setting new limits on TeV-scale compositeness scenarios involving broad resonances.
This paper introduces Alkaid, an open-source domain-specific compiler that translates sub-microsecond dataflow kernels into hardware-ready RTL or HLS code using a specialized intermediate representation and analytical performance model to achieve significant reductions in latency and resource usage compared to existing flows.
This paper presents a machine-learning-based framework for identifying highly boosted, leptonically decaying top quarks at the 100 TeV FCC-hh, demonstrating that both variable-based and constituent-based taggers achieve comparable performance and enabling a search for vector-like quarks with a discovery reach of up to 10 TeV.
This paper presents the first data-driven Bayesian unfolding analysis of open beauty hadron production in p-Pb collisions at 5.02 and 8.16 TeV, providing precise, full-rapidity nuclear modification factors that reveal weaker cold nuclear matter suppression than charm and offer critical constraints for nuclear parton distribution functions.
The paper argues that observed LHC excesses at 650 GeV and 95 GeV provide evidence for a Randall-Sundrum Kaluza-Klein graviton sequence previously thought to be too heavy for detection, suggesting that reanalyzing data with appropriate selection criteria for these resonances in ZZ, WW, and Higgs pair channels could lead to a significant discovery.
This paper details the BMV experiment's apparatus and reports an improved upper limit on the vacuum linear magnetic birefringence constant of at a 68% confidence level, achieved using specially designed pulsed Foil Coils.
This paper presents a deep learning-based graph neural network prototype for the FCC-ee IDEA detector that successfully reconstructs hierarchical -hadron decay trees from charged particle data, achieving over 90% accuracy in identifying the correct particle sets across various decay chains.
This paper introduces SysVar, an open-source, experiment-agnostic Python library that simplifies the scalable and consistent treatment of systematic uncertainties in template-based analyses by automating the propagation of correction weights and preserving correlations through orthogonal eigenvariations.
This paper presents a proof-of-concept study demonstrating that Neural Ratio Estimation (NRE) can effectively infer WIMP parameters in direct detection experiments like XENONnT by implicitly marginalizing over astrophysical uncertainties through multi-model training, offering a scalable and robust alternative to traditional profile likelihood methods.