2092 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.
Using LHCb data from $pp$ collisions at 13 TeV, this paper reports the first observation of the Cabibbo-suppressed decay , measuring its branching fraction to be and determining its decay asymmetry parameter to be .
This study systematically compares classical and quantum machine learning architectures for regression on simulated high-energy physics collision data, finding that while classical models like CNNs and LSTMs currently offer marginally better performance, quantum counterparts achieve competitive accuracy with significantly fewer trainable parameters, highlighting a distinct parameter-efficiency advantage for near-term quantum devices.
This editorial outlines a research initiative aimed at advancing green computing in high-energy physics and astrophysics by evaluating current energy consumption, identifying optimization opportunities, and fostering cross-disciplinary collaboration to enhance sustainability.
This paper evaluates and compares classical and graph-neural-network-based methods for reconstructing annihilation vertices in the HIBEAM experiment, finding that while both approaches achieve comparable accuracy for vertex coordinates, machine learning offers unique advantages in extracting event-shape information for downstream analysis.
Using 138 fb of proton-proton collision data at = 13 TeV collected by the CMS detector, this study presents the first search for TeV-scale scalar leptoquarks produced via muon-quark scattering, finding no evidence for their existence and setting new exclusion limits that extend previous coverage to masses between 1.5 and 3.6 TeV for LQ(u) and above 1.8 TeV for LQ(b).
This thesis presents the first full-phase-space, high-dimensional measurement of the Z+jets differential cross section at the LHC using the ATLAS experiment, demonstrating how artificial intelligence-based unfolding techniques can fully exploit complex datasets to characterize fundamental particle interactions.
This paper presents next-to-next-to-next-to-leading logarithmic (NLL) resummation combined with fixed-order corrections to predict lepton-jet azimuthal angular distributions in deep-inelastic scattering, offering a precision framework for probing the nucleon's three-dimensional structure and analyzing data from HERA and future electron-ion collider experiments.
Using a large sample of events collected by the BESIII detector, this study reports the first measurement of the absolute branching fraction and axial-vector to vector coupling ratio for the semileptonic decay , achieving a branching fraction significantly lower than the world average and an axial charge precision comparable to previous experiments despite using only 5% of their statistics.
This paper analyzes Belle II data to demonstrate that a light invisible resonance with a mass of approximately 2.1 GeV provides a compelling explanation for the observed excess in decays, significantly favoring this new-physics hypothesis over the Standard Model-only scenario.
This study benchmarks four classical and four hybrid quantum machine learning models on an eight-qubit, PCA-constrained CMS open data trigger task, finding that while the classical artificial neural network outperforms all quantum counterparts in accuracy and ROC-AUC, trainable hybrid quantum embeddings (specifically the quantum convolutional network) achieve competitive results, serving as a controlled reference point rather than a demonstration of quantum advantage.