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.
Please use this link to access the test for this project. The test is due by April 3rd, however please keep in mind that it takes about 1 week to craft a good proposal and proposals need to be submitted via GSoC portal by April 4
Python, C++, and some previous experience in Machine Learning.
Please DO NOT contact mentors directly by email. Instead, please email email@example.com with Project Title and include your CV and test results. The mentors will then get in touch with you.