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.
This project will focus on the development of end-to-end deep learning regression for estimating particle properties and CMSSW inference engine for use in reconstruction algorithms in offline and high-level trigger systems of the CMS experiment.
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
Please DO NOT contact mentors directly by email. Instead, please email firstname.lastname@example.org with Project Title and include your CV and test results. The mentors will then get in touch with you.