Hep-Ph explores the fundamental forces that govern how particles interact and behave at the smallest scales imaginable. This field bridges the gap between theoretical predictions and experimental reality, helping scientists understand the building blocks of our universe without getting lost in complex mathematics. Whether investigating the Higgs boson or searching for new physics beyond current models, these studies push the boundaries of human knowledge about matter and energy.

At Gist.Science, we process every new preprint in this category as soon as it appears on arXiv. We strip away the dense jargon to offer both accessible plain-language explanations and detailed technical summaries, ensuring that groundbreaking research is understandable to everyone from students to seasoned experts. Below are the latest papers in this dynamic field, ready for you to explore with clarity and depth.

⚛️ phenomenology

Gamma-Ray Echo as a Probe of Supernova Neutrino Emission Anisotropy and Long-Baseline Effects

This paper proposes that comparing the electron antineutrino flux inferred from a core-collapse supernova's gamma-ray echo with that detected on Earth can serve as a powerful probe for testing large neutrino emission anisotropies and long-baseline propagation effects, thereby motivating the development of next-generation MeV gamma-ray telescopes.

Garv Chauhan, Cecilia Lunardini, Yago Porto2026-09-22
⚛️ nuclear theory

QCD thermodynamics through the crossover as a gas of confining strings with repulsive interactions

This paper demonstrates that QCD thermodynamics in the intermediate-temperature regime can be effectively described by a gas of confining strings with repulsive excluded-volume interactions, where an open-string spectrum characterized by a Hagedorn temperature of approximately 300 MeV and distinct mesonic and baryonic volume parameters successfully reproduces bulk lattice-QCD data above the pseudocritical temperature.

Yuki Fujimoto, Volodymyr Vovchenko2026-09-22
⚛️ phenomenology

LLM-Based FORM Code Generation with Verification-Driven Fine-Tuning

This paper addresses the lack of AI support for the FORM symbolic manipulation language by demonstrating that frontier large language models fail at zero-shot FORM tasks, then introduces a verification-driven fine-tuning pipeline using the FORM binary as an oracle to train a compact 8B-parameter model that outperforms much larger frontier models in code execution and correctness while preserving general reasoning capabilities.

Bakar Chargeishvili2026-09-22