⚛️ Category

hep-ex

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.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows

This paper introduces a novel amortized inference technique using likelihood-weighted Normalizing Flows that overcomes the limitations of standard unimodal base distributions in capturing multi-modal posteriors by initializing the flow with a Gaussian Mixture Model, thereby enabling efficient and accurate parameter estimation in high-dimensional inverse problems without requiring posterior training samples.

Rajneil Baruah2026-02-23⚛️ hep-ex

Development and Application of an eV Neutron Polarization for Parity Violation Studies at CSNS Back-n Beamline

Researchers at the China Spallation Neutron Source successfully developed and validated an eV-range polarized neutron system using an in-situ 3^3He Spin-Exchange Optical Pumping filter and solenoidal guide field, achieving a measured parity violation asymmetry of 7.8±2.47.8 \pm 2.4% at the 0.747 eV resonance of 139^{139}La to support future Time-Reversal Invariance Violation studies.

Xu Qin, Tianhao Wang, Xuanbo Chen, Changdong Deng, Yongce Gong, Zenghang Huang, Wei Jiang, Zhengquan Liu, Guangyuan Luan, Haotian Luo, Qiuyue Luo, Yongjia Lv, You Lv, Nikolaos Vassilopoulos, Xichao Ru (…)2026-02-23⚛️ nucl-ex

Hardware-Aware Design of a GNN-Based Hit Filtering Algorithm for the Belle II Level-1 Trigger

This paper presents a hardware-aware model-compression workflow for a GNN-based hit-filtering algorithm deployed on FPGAs in the Belle II Level-1 trigger, achieving a reduction in computational cost by over two orders of magnitude through techniques like 4-bit fixed-point arithmetic and pruning while maintaining high hit efficiency and background rejection performance.

Greta Heine, Fabio Mayer, Marc Neu, Jürgen Becker, Torben Ferber2026-02-23⚛️ hep-ex

Search for a new resonance decaying to a Higgs boson and a scalar boson in events with two b jets and two Z bosons in proton-proton collisions at s\sqrt{s} = 13.6 TeV

Using 138 fb−1^{-1} of proton-proton collision data at s\sqrt{s} = 13 TeV, a search for new resonances decaying into Higgs boson pairs or a Higgs boson and a scalar boson in the bbZZ final state found no significant deviations from Standard Model predictions, resulting in the establishment of 95% confidence level upper limits on the production cross sections for these processes.

CMS Collaboration2026-02-23⚛️ hep-ex

Long-range transverse momentum correlations and radial flow in Pb$-$Pb collisions at the LHC with ALICE

This ALICE study introduces the new observable v0(pT)v_{0}(p_\mathrm{T}) to measure long-range transverse momentum correlations in Pb-Pb collisions at 5.02 TeV, revealing mass-dependent flow patterns consistent with hydrodynamics and quark recombination while demonstrating its sensitivity to bulk viscosity and the equation of state for constraining the properties of strongly interacting matter.

ALICE Collaboration2026-02-20⚛️ hep-ex

A Practical Guide to Unbinned Unfolding

This paper provides practical recommendations and considerations from researchers across major particle physics experiments on adopting emerging machine learning-based unbinned unfolding techniques to replace traditional binned histogram methods for more flexible, high-dimensional data analysis.

Florencia Canelli, Kyle Cormier, Andrew Cudd, Dag Gillberg, Roger G. Huang, Weijie Jin, Sookhyun Lee, Vinicius Mikuni, Laura Miller, Benjamin Nachman, Jingjing Pan, Tanmay Pani, Mariel Pettee, Youqi S (…)2026-02-20⚛️ hep-ex