Speaker
Description
Machine learning is playing an increasingly important role in particle physics, from offline data analysis to real-time event selection at the Large Hadron Collider. The COST Action EPIGRAPHY (Edge deeP learnIng foR pArticle PHYsics) brings together researchers across Europe to advance efficient deep learning methods for resource-constrained, low-latency computing environments. This contribution will provide an overview of the activities of the EPIGRAPHY network, with a particular focus on the development of a community-wide machine learning benchmark and data challenge. The initiative defines a suite of standardised online and offline challenges covering a broad range of physics tasks, accompanied by a common dataset (Collide-2V), evaluation metrics, baseline models, and submission infrastructure. The online challenges additionally incorporate realistic FPGA latency and resource constraints, enabling fair comparison of algorithms for edge deployment.