Exploring the underlying symmetries in particle physics with equivariant neural networks

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.

Duration

Total project length: 175/350 hours.

Task ideas

Develop suitable equivariant neural network models and use them to analyze the collision data
Benchmark the equivariant neural network with vision transformers and convolution-based models
Extend the idea of Equivariant Neural Networks to exploit symmetries to finding and understanding symmetries and asymmetries in the data

Expected results

Achieve competitive results using equivariant neural networks
Benchmark the models with other previous works in terms of speed, performance and data efficiency.

Requirements

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

Test

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

Mentors

Emanuele Usai (University of Alabama)
Ruchi Chudasama (University of Alabama)
Shravan Chaudhari (BITS Pilani Goa)
Sergei Gleyzer (University of Alabama)
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.