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.

⚛️ high-energy experiments

GRACE: an Agentic AI for Particle Physics Experiment Design and Simulation

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.

Justin Hill, Hong Joo Ryoo2026-02-18
⚛️ high-energy experiments

Excluding MeV-scale QCD axions by KL→π0π0aK_L \to π^0π^0 a at KTeV

This paper reexamines the viability of MeV-scale QCD axions by deriving new constraints from kaon decay measurements, particularly the KTeV KL→π0π0e+e−K_L \to \pi^0 \pi^0 e^+ e^- data, and concludes that the previously suggested parameter window is effectively excluded even after accounting for theoretical uncertainties.

Takaya Iwai, Ryosuke Sato, Kohsaku Tobioka, Takumu Yamanaka2026-02-18
⚛️ high-energy experiments

New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis

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.

Jeffrey Lazar, Santiago Giner Olavarrieta, Giancarlo Gatti, Carlos A. Argüelles, Mikel Sanz2026-02-17