One of the important aspects of searches for new physics at the Large Hadron Collider (LHC) involves the identification and reconstruction of single particles, jets and event topologies of interest in collision events. The End-to-End Deep Learning (E2E) project in the CMS experiment focuses on the development of these reconstruction and identification tasks with innovative deep learning approaches.
One potential approach for particle reconstruction is to take minimally processed detector hit information from jets of decayed particles and rebuild the tracks that the originating particles followed and derive further quantities from those tracks. This project will focus on using machine learning models to achieve this reconstruction.
Total project length: 175/350 hours.
Intermediate
C++, Python, PyTorch and some previous experience in Machine Learning.
Please use this link to access the test for this project.
Please DO NOT contact mentors directly by email. Questions should instead be directed to ml4-sci@cern.ch which is forwarded to mentors. To submit your proposal, CV, and test task solutions, please use this Google form.