Skip to main content
Browse by:

Joint HEP/Theory Seminar: New machine learning algorithms for the CMS level-1 trigger at the CERN LHC

Event Image
Tuesday, April 30, 2024
3:00 pm - 4:00 pm
Melissa Quinnan, UCSD
Joint HEP/Theory Seminar

Recent advancements in computer science and tools are making the ability to deploy machine learning (ML) algorithms in firmware increasingly powerful and accessible. One such tool is hls4ml, a source-to-source compiler for translating ML algorithms into firmware for field-programmable gate arrays (FPGAs). This has exciting implications for the development of novel ML-based level-1 (L1) triggers implemented on FPGAs for particle physics experiments like CMS at the CERN LHC. For example, ML-based anomaly detection methods like variational autoencoders (VAEs) have been gaining popularity as a way of extracting potential new physics signals in a model-agnostic way. These unsupervised algorithms can be trained on unlabeled data rather than simulations, making them a good candidate for anomaly detection triggers. I will describe two VAE-based anomaly detection algorithms, "AXOL1TL" and "CICADA", that are expected to be deployed in the CMS L1 trigger this year as the very first neural-network-based algorithms on CMS L1 FPGAs. We also briefly discuss ideas for future ML trigger algorithms that are expected to be deployed following a similar strategy for the high-luminosity LHC.

Contact: Kate Scholberg