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.

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.

Links

• Paper 1
• Paper 2

Corresponding Project

• CMS

Participating Organizations

• Alabama
• New York University

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