1327 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 paper introduces **da4ml**, an efficient distributed arithmetic algorithm integrated into the `hls4ml` library that reduces FPGA resource utilization by up to one-third while simultaneously decreasing latency for real-time, highly quantized neural network inference.
DeepMET is a novel missing transverse momentum estimator developed by the CMS Collaboration that utilizes deep neural networks to assign weights to reconstructed particles, resulting in improved resolution, greater versatility across final states, and increased resilience to pileup compared to existing methods.
This paper presents a generic method using Boosted Decision Trees to multi-dimensionally reweight existing neutrino Monte Carlo simulations to match a target model, allowing for efficient reuse of legacy data without the need for new event generation.
This paper introduces a novel end-to-end muon tracking approach that utilizes differentiable programming to integrate physics priors directly into a machine learning model, combining graph attention networks with differentiable clustering and fitting to simultaneously improve hit selection and transverse momentum estimation.
This paper proposes a new measurement technique that combines jet charge with global event shapes (specifically 1-jettiness in DIS) to simultaneously probe quark flavor dynamics in both initial-state nucleon structure and final-state hadronization processes.
This paper presents the development and testing of lightly doped amorphous silicon resistors designed for ultra-high radiopurity, mechanical stability, and cryogenic performance to be used in low-background detectors like the nEXO experiment.
jBOT is a self-distillation pre-training method for LHC jet data that learns semantic representations by combining local particle-level and global jet-level distillation, enabling emergent class clustering that improves performance in downstream anomaly detection and classification tasks.
This paper provides a unified theoretical framework for the angular structure of energy-energy correlators (EECs) in heavy-ion collisions, demonstrating how they transition from being dominated by collective hydrodynamic flow at large angles to being controlled by hydrodynamic modes and light-ray operator product expansions at smaller angles.
This paper presents the first measurement of the angular distribution parameters, specifically the forward-backward asymmetry () and the flat term (), for the decay using LHCb data, finding results that are largely consistent with Standard Model predictions.
This paper investigates how solar-reflected inelastic dark matter, which is accelerated by solar electrons, can produce detectable energy signals in terrestrial experiments, potentially allowing for new constraints on MeV-scale dark matter models.