1495 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 demonstrates that a team of large language model (LLM) agents can autonomously generate code to solve complex particle physics data analysis tasks, specifically anomaly detection on the LHC Olympics dataset, achieving performance comparable to human state-of-the-art results.
This paper demonstrates that NLO calculations are essential for achieving reliable predictions of the irreducible gluon-fusion background to vector boson fusion Higgs production, while providing consistent simulation setups to resolve discrepancies among existing event generators.
This paper proposes using topological anomalies in the process to probe the strange-quark electric dipole moment, estimating that current and future experiments like CMD-3, BESIII, Super Tau-Charm, and Belle II could achieve sensitivities ranging from to .
This paper introduces GRACE, an agentic AI system that autonomously designs and optimizes particle physics experiments by extracting structured representations from natural language or papers, constructing simulations, and iteratively proposing and evaluating detector modifications using first-principles Monte Carlo methods to improve physics performance under physical and budgetary constraints.
This paper reexamines the viability of MeV-scale QCD axions by deriving new constraints from kaon decay measurements, particularly the KTeV data, and concludes that the previously suggested parameter window is effectively excluded even after accounting for theoretical uncertainties.
This paper presents the first implementation of a real-time Graph Neural Network on an FPGA for the Belle II electromagnetic calorimeter trigger, which achieves 8 MHz throughput with 3.168 μs latency while significantly improving position resolution, cluster purity, and efficiency compared to the baseline algorithm.
This paper computes charm and strange meson fragmentation functions by deriving and solving a set of twenty-five coupled jet equations based on Poincaré covariant Bethe-Salpeter wave functions, thereby providing a consistent description of quark hadronization across both light and heavy sectors.
This paper demonstrates the first viable, ultra-fast machine learning application on radiation-hard FPGAs by developing a lightweight autoencoder for the PicoCal calorimeter and extending the hls4ml library with a new backend to synthesize the model for Microchip PolarFire devices, achieving a 25 ns latency with minimal resource usage.
This paper proposes a quantum-encoded data analysis methodology using parity observables on an 8-qubit processor to compress and recover neutrino telescope event information with 84% fidelity, enabling the classification of electron- and muon-neutrino events to overcome the limitations of traditional triggers and the "street light effect" in particle physics.
This paper proposes and analyzes two minimal, ultraviolet-complete tri-hypercharge models that explain Standard Model fermion masses and mixings through three correlated physical scales, predicting that the lightest boson in these theories is discoverable via dilepton searches at LHC Run 3.