Next generation vision transformers for end to end mass regression and classification

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.

A minimal representation of particle collision data is as an image representation of particle hits in different layers of the detector. This project will explore development of vision transformers incorporating the latest knowledge in the field of computer vision to classify particle collision images by the type of heavy particles generated in the collision and reconstruct the mass of those particles.

Duration

Total project length: 175/350 hours.

Difficulty level

Intermediate

Task ideas

Development of vision transformer models for end-to-end classification and regression

Expected results

Trained vision transformer models incorporating the latest computer vision techniques
Benchmarks against baseline vision transformers and comparison of different vision transformer types

Requirements

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

Test

Please use this link to access the test for this project.

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. 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.