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.
The HAWL and HAWLU projects demonstrate the capabilities of dynamic muography by successfully mapping mountainous terrain with a portable, vehicle-mounted detector and measuring tidal water-level variations in an undersea tunnel with meter-scale precision using a compact, upgraded system.
Using 140 fb of proton-proton collision data at TeV, the ATLAS collaboration reports the first single-experiment observation of single top-quark production in the -channel with a significance of 5.1 standard deviations, measuring a cross-section consistent with Standard Model predictions.
This paper computes the flavor-separated chiral-odd nucleon tensor form factors and their resulting monopole, dipole, and quadrupole moments using light-cone QCD sum rules, revealing new exact analytical relations between quark flavors and providing numerical predictions for the transverse spatial deformation of the nucleon's and quarks.
This paper presents the design and initial performance results of the 112-tonne SBND liquid argon time projection chamber, demonstrating that all systems met or exceeded design requirements—including exceptional argon purity, near-theoretical noise levels, and high data collection efficiency—thereby establishing the detector as a state-of-the-art instrument for neutrino physics.
This paper introduces a Geometric Deep Learning framework utilizing Graph Neural Networks to enhance the detection and reconstruction of the Diffuse Supernova Neutrino Background in water-Cherenkov detectors by accurately recovering positron energy and classifying neutron capture signals with performance comparable to state-of-the-art methods.
This paper proposes leveraging known detector geometries to optimize the computational complexity of Graph Neural Networks for high-granularity calorimeter reconstruction, validating the approach across multiple tasks and introducing HIBOU, a modular architecture for integrating these optimized GNN blocks into experiment-specific frameworks.
This paper reports the first demonstration of 4D vertex reconstruction at the 50 ps scale using a Timepix4 beam telescope, achieving a vertex time resolution of for 16 clusters and identifying clock distribution and time reference systems as the primary irreducible contributors to the remaining resolution limit.
This paper presents a combined analysis of atmospheric neutrino data from Super-Kamiokande, IceCube-DeepCore, and KM3NeT/ORCA, which achieves competitive precision on oscillation parameters and excludes the inverted neutrino mass ordering at a 99.95% confidence level in favor of the normal ordering.
This paper demonstrates that interpreting charged-lepton flavor violation limits as probes of -sector new physics requires simultaneously accounting for both dimension-6 renormalization-group running and dimension-8 operator mixing, as neglecting either mechanism leads to significant errors in constraining the underlying Wilson coefficients.
This paper introduces a highly compact, quantum-inspired Tree Tensor Network jet tagger that leverages a shared tensor core and strong radiation priors on Lund declustering trees to achieve performance comparable to much larger models in quark/gluon discrimination and top tagging, while enabling unique interpretability through latent space analysis across tree recursions and generator transfer.