Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System


CMS experiment currently uses machine learning algorithms at the Level-1 (hardware) trigger to estimate the momentum of traversing particles such as Muons. The first algorithm implemented in the trigger system was a discretized boosted decision tree. Currently, CMS is studying the use of deep learning algorithms at the trigger level that requires microsecond level latency and therefore requires highly optimized inference.

This project will focus on implementation and benchmarking of deep learning algorithms for the trigger inference task.


Total project length: 175/350 hours.

Task ideas

Expected results


Python, C++, and some previous experience in Machine Learning.


Please DO NOT contact mentors directly by email. Instead, please email with Project Title and include your CV and test results. The mentors will then get in touch with you.

Corresponding Project

Participating Organizations