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 to estimate the particle properties, such as mass of a particle.

Duration

Total project length: 175/350 hours.

Task ideas

• Development of end-to-end deep learning regression using vision transformer or graph neural networks for particle property measurements.

Expected results

• Trained end-to-end deep learning regression model for estimating the properties of a simulated top quark pair event

Difficulty level

Intermediate

Requirements

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

Mentors

• Ruchi Chudasama (University of Alabama)
• Emanuele Usai (University of Alabama)
• Michael Andrews(Carnegie Mellon University)
• Sergei Gleyzer (University of Alabama)
• Eric Reinhardt (University of Alabama)
• Colin Crovella (University of Alabama)
• Bhim Bam (University of Alabama)

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.

Links

• Paper 1

Corresponding Project

• E2E

Participating Organizations

• Alabama
• CMU

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