Uncovering Singularities in Feynman Integrals via Machine Learning
High Energy Physics - Phenomenology
2025-10-28 v2 Artificial Intelligence
Machine Learning
High Energy Physics - Theory
Abstract
We introduce a machine-learning framework based on symbolic regression to extract the full symbol alphabet of multi-loop Feynman integrals. By targeting the analytic structure rather than reduction, the method is broadly applicable and interpretable across different families of integrals. It successfully reconstructs complete symbol alphabets in nontrivial examples, demonstrating both robustness and generality. Beyond accelerating computations case by case, it uncovers the analytic structure universally. This framework opens new avenues for multi-loop amplitude analysis and provides a versatile tool for exploring scattering amplitudes.
Cite
@article{arxiv.2510.10099,
title = {Uncovering Singularities in Feynman Integrals via Machine Learning},
author = {Yuanche Liu and Yingxuan Xu and Yang Zhang},
journal= {arXiv preprint arXiv:2510.10099},
year = {2025}
}