1495 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 utilizes QCD light-cone sum rules with high-twist and next-to-leading-order corrections to reanalyze transition form factors, confirming chiral enhancement effects and extracting optimal - mixing parameters that are strongly favored by recent BESIII experimental data.
This paper evaluates the sensitivity of future and colliders to top quark Flavor Changing Neutral Currents within Randall-Sundrum models, incorporating current and projected HL-LHC limits to determine that while the HL-LHC may reach branching ratios of , high-energy lepton colliders offer the potential to probe even smaller coupling strengths.
This paper presents measurements of identical charged-kaon femtoscopic correlations in Pb-Pb collisions at TeV, revealing that the extracted emission source radii decrease with increasing collision centrality and pair transverse momentum—a trend attributed to collective flow and well-described by hydrokinetic models, which also indicate that kaons are emitted earlier in more peripheral collisions.
This paper presents the first measurement of three-dimensional femtoscopic correlations between identical charged kaons in p$-$Pb collisions at TeV, revealing that source sizes increase with multiplicity and decrease with transverse momentum, align with trends in other collision systems, and indicate a kaon emission evolution comparable to peripheral Pb$-$Pb collisions.
This paper presents measurements of inclusive W and Z boson production cross sections and their ratios in proton-proton collisions at a center-of-mass energy of 13.6 TeV using 2022 data, yielding results that are in agreement with next-to-next-to-leading order quantum chromodynamic predictions.
Using 138 fb of proton-proton collision data at = 13 TeV collected by the CMS detector, this study searches for -channel scalar and vector leptoquark exchange in high-mass dilepton spectra, setting stringent 95% confidence level limits on leptoquark-fermion couplings for masses between 1 and 5 TeV.
This paper presents a novel fast simulation tool for the LHCb experiment that utilizes heterogeneous graph neural networks to emulate detector responses for arbitrary multibody decay topologies, enabling generalizable interpolation to unseen channels and offering a scalable solution to the growing computational demands of particle physics simulations.
This paper demonstrates the application of Truncated Marginal Neural Ratio Estimation (TMNRE) within the Swyft framework to constrain axion-like particle parameters using simulated Cherenkov Telescope Array observations of NGC 1275, showcasing a robust alternative to standard likelihood-based methods for handling complex models with numerous nuisance parameters.
This paper reports the first muon detection efficiency measurements and demonstrates a 75% fill factor-normalized efficiency with 130 ps time resolution for an 8-channel WSi superconducting microwire array, marking a significant step toward high-efficiency charged particle tracking systems for future accelerator experiments.
This paper introduces DUNE-GPT, a prototype framework that utilizes large language models and retrieval-augmented generation to enable secure, natural-language querying of the Deep Underground Neutrino Experiment's extensive documentation and technical resources, thereby enhancing collaboration productivity and onboarding.