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 hours.
Please use this link to access the test for this project.
Python, C++, and some previous experience in Machine Learning.
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