2101 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 identifies the origin of perturbative instabilities in NNLO QCD corrections to quarkonium production and decay within non-relativistic QCD and proposes a simple remedy involving soft contributions that significantly improves convergence and agreement with experimental data for S-wave color-singlet processes.
This paper demonstrates that transverse-longitudinal polarization interference in exclusive jets processes vanishes upon phase-space integration for decays but is softened by parity violation for and bosons, providing a framework to account for fiducial cuts and implications for new physics and heavy ion collisions.
This paper introduces the Minimum Resolution Likelihood (MRL) method, which defines a Fiducial Signal Region to convert systematic uncertainties into statistical ones, thereby ensuring unbiased signal strength estimation in collider analyses that utilize Machine Learning models.
Using 8.5 fb⁻¹ of collision data collected by the BESIII detector at center-of-mass energies between 4.42 and 4.95 GeV, this study reports no significant signal for the process, instead establishing 90% confidence level upper limits on its Born cross sections and finding no evidence for intermediate charmonium-like resonances in the invariant-mass spectrum.
This paper proposes searching for the structure via the reaction using an effective Lagrangian approach to determine its nature as a genuine resonance or molecular state, free from the interference effects present in collisions.
This paper proposes that a recent ATLAS excess in the triple Higgs mass region, potentially corresponding to predicted Randall-Sundrum KK graviton resonances at 1000 and 1300 GeV, offers a promising pathway to confirm the existence of these extra resonances and constrain the KK graviton coupling parameter using upcoming LHC Run 3 data.
This paper presents the design of the LHCb UP MAPS-based pixel tracker and demonstrates that its dedicated standalone reconstruction algorithms achieve high efficiency (~98%) and low ghost rates (<4%), offering a robust solution for future high-luminosity tracking challenges.
This paper introduces Factorizable Normalizing Flows (FNFs), a scalable and interpretable framework that models parameter-dependent density deformations by combining a fixed reference flow with a learnable, factorized polynomial transformation, thereby enabling efficient inference without the computational intractability of sampling exponentially large joint parameter spaces.
This paper interprets nine statistically significant LHC decay channels around 650 GeV as evidence for a sequence of Beyond-the-Standard-Model resonances, specifically a spin-2 Kaluza-Klein graviton (T700) and a wide scalar (H650), which align with Randall-Sundrum and composite model predictions and offer promising targets for future electron-positron colliders.
This paper proposes an alternative test statistic to the standard likelihood ratio test that allows particle physicists to focus statistical power on specific, physics-motivated regions of the parameter space, demonstrating improved performance in Higgs and dark matter searches while utilizing machine learning to efficiently calibrate critical values for valid confidence intervals.