End-to-End Deep Learning Regression for Measurements with the CMS Experiment

Description

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.

Duration

Total project length: 175/350 hours.

Task ideas

Development of end-to-end deep learning regression for particle property measurements
Test and integration into CMSSW

Expected results

Trained end-to-end deep learning regression model for estimating the properties of a simulated top quark pair event
Extension of currently integrated E2E CMSSW prototype to include the regression model inference

Requirements

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

Mentors

Ruchi Chudasama (University of Alabama)
Shravan Chaudhari (New York University)
Emanuele Usai (University of Alabama)
Michael Andrews (Carnegie Mellon University)
Eric Reinhardt (University of Alabama)
Samuel Campbell (University of Alabama)

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.