2527 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 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.
Using 140 fb of 13 TeV proton-proton collision data, the ATLAS experiment conducted two searches for higgsinos with compressed mass spectra by identifying low-momentum tracks from displaced pions or prompt leptons, setting new world-leading lower limits of 126 GeV on chargino masses for mass splittings between 0.3 and 2 GeV without observing any significant excess over Standard Model predictions.
This paper presents the first Belle II measurement and an updated Belle measurement of the branching fraction, combining them to achieve the most precise search to date with a 2.4 significance and a 90% confidence level upper limit of .
This paper presents a phenomenological study demonstrating that production at the LHC is sensitive to light spin-1 dark matter mediators and that CP-sensitive angular observables can effectively distinguish between vector, axial-vector, scalar, and pseudoscalar mediator hypotheses.
This paper presents a femtoscopic analysis of proton-proton correlations in minimum bias $pp$ collisions at TeV using a high-luminosity ALICE dataset, which enables the first simultaneous measurement of the hadron-emitting source size's dependence on multiplicity and transverse mass, revealing a distinct multiplicity trend compared to Pb-Pb collisions and demonstrating the robustness of the results across various nucleon-nucleon interaction models.
This paper presents the first global analysis of unpolarized Transverse-Momentum-Dependent (TMD) distributions using a neural-network parametrization that simultaneously incorporates Drell-Yan and SIDIS data at NLL accuracy, demonstrating that the inclusion of SIDIS data broadens the extracted TMDs while reducing uncertainties compared to Drell-Yan-only analyses and offering a model-independent framework for future high-precision experiments.