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

Description

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.

Duration

Total project length: 175 hours.

Task ideas

Expected results

Test

Please use this link to access the test for this project.

Requirements

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

Mentors

Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.

Corresponding Project

Participating Organizations