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 foundation models for end-to-end particle reconstruction with the goal of performing generative, classification and regression tasks.

Duration

Total project length: 175/350 hours.

Difficulty level

Advanced

Task ideas

• Develop a pre-training strategy for learning robust representations of high-energy physics detector data.
• Fine-tune the foundation model for multiple tasks such as classification, generative, super-resolution, and regression.
• Evaluate the model’s performance on different HEP datasets and benchmark against individual methods for classification, regression etc.

Expected results

• Trained foundation model and benchmarks

  Requirements

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

Mentors

• Ruchi Chudasama (University of Alabama)
• Shravan Chaudhari (New York University)
• Sergei Gleyzer (University of Alabama)
• Purva Chaudhari (Vishwakarma Institute of Technology)
• Diptarko Choudhury (NISER)

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
• NISER
• New York University
• Vishwakarma Institute of Technology

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