Meet the students: Francisco Resende

How can machine learning boost the performance of ATLAS’s real-time event selection? That’s the question driving the work of Francisco Resende, a CERN openlab summer student from the Technical University of Munich. As part of the Next Generation Triggers project, his work focuses on bringing AI techniques into the upgraded muon trigger system for the High-Luminosity LHC.

The muon trigger is essential for identifying rare physics events at record collision rates, and for the first time it will integrate precision data from monitored drift-tube (MDT) chambers at the earliest stage of event selection. Meeting this challenge requires innovative approaches to data processing and algorithm design.

Francisco is developing and training neural networks to improve muon reconstruction, optimising algorithms for deployment on FPGA devices, and benchmarking machine learning–based methods against traditional analytic algorithms.

By bridging artificial intelligence and high-speed detector electronics, Francisco’s work with Next Generation Triggers is helping ATLAS push the boundaries of discovery in the HL-LHC era.