1387 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 a new technique using two multivariate models to identify highly boosted dielectrons that merge into a single electromagnetic calorimeter cluster in 13 TeV proton-proton collisions, achieving 80% and 60% efficiencies for cases with two and one reconstructed tracks, respectively, alongside a dedicated energy correction method.
This paper demonstrates that a ResNet-based machine learning framework (WatChMaL) can achieve particle classification and kinematic reconstruction for Hyper-Kamiokande events with accuracy comparable to traditional methods while offering a massive speed-up of over 30,000 times, thereby enabling the processing of the experiment's required large-scale Monte Carlo datasets.
This paper demonstrates that a future 10 TeV muon collider, utilizing a multivariate analysis of the process with of integrated luminosity, can achieve projected sensitivities to top-quark flavor-changing neutral current couplings at the level, thereby improving upon current LHC bounds by more than an order of magnitude.
This paper reviews recent precision studies of production and decay properties conducted by the BESIII experiment using the world's largest and data sets to advance the understanding of charmonium structure and strong interactions in the charm sector.
This study utilizes a second-order relativistic viscous hydrodynamic framework to quantify the contributions of thermal vorticity and evolving magnetic fields to the global spin polarization of hyperons, finding qualitative agreement with recent ALICE measurements at TeV energies and offering new insights into the vortical structure of QCD matter.
This paper proposes a method to predict the positions of all photomultiplier tubes in the JUNO detector based on limited survey data of its deformed structure, demonstrating that incorporating this realistic geometry into reconstruction models eliminates significant vertex biases while confirming that the physical deformation itself has a negligible impact on energy resolution.
The STAR experiment at RHIC provides strong evidence for jet quenching in oxygen-oxygen collisions at GeV by observing a significant 20% suppression of high-transverse-momentum hadron and jet yields in high-event-activity collisions, indicating the formation of a quark-gluon plasma even in small collision systems.
Using 138 fb⁻¹ of 13 TeV and 35 fb⁻¹ of 13.6 TeV proton-proton collision data, this study presents the first search for beyond-the-Standard-Model resonances in four-top-quark production via the two-lepton channel, finding no significant excess and setting exclusion limits on various mediator models, including Z' bosons up to 850 GeV.
This paper investigates three types of new physics resonances that couple to Standard Model quarks through direct top-quark flavor-changing interactions, identifying the relevant SMEFT operators at the electroweak scale and analyzing their phenomenological implications.
This paper presents a global Bayesian analysis of unpolarized quark transverse-momentum-dependent parton distribution functions using Drell-Yan data at and accuracy, leveraging AI-driven functional form selection and machine-learning emulators to enable efficient Markov Chain Monte Carlo sampling and quantify uncertainties.