"Symmetry and Geometry in Machine Learning for Particle Physics"
Artificial Intelligence (AI) and Machine Learning (ML) have become critical tools in many scientific research domains. Particularly, in data and statistics heavy fields like particle physics, ML tools are essential to meeting the computing needs of current and future experiments and to ensuring robust data reconstruction and interpretation. This relationship is reciprocal as well as incorporating symmetries, conservation laws, and statistical methodologies from physics have led to advances in state of the art ML. In this talk I will discuss my work using geometric machine learning for data reconstruction at the Large Hadron Collider. In particular, I will introduce ways of incorporating symmetry conservation into graph neural networks and present our recent studies focused on characterizing the utility of enforced equivariance when applying ML to real world physics problems. Additionally, I'll discuss ways that physicists are uniquely equipped to contribute to the broader space of ML/AI research.