Description

One of the most important physical quantities in particle physics is the cross section, or a probability that a particular process takes place in the interaction of elementary particles. Its measure provides a testable link between theory and experiment. It is obtained theoretically mainly by calculating the squared amplitude. The approach we use in this project is to treat the amplitude and squared amplitude as mathematical symbolic expressions and use language-translation models to map from the amplitude to squared-amplitude.

Duration

Total project length: 175/350 hours.

Task ideas and expected results

• Develop various transformer-based models on sequence-to-sequence tasks
• Benchmark different models on simulated physics datasets of various complexity and sequence lengths to find the best model
• Integrate with the SymbaHEP pipeline

Requirements

Significant experience with Transformer machine learning models in Python (preferably using pytorch).

Difficulty Level

Advanced

Mentors

• Eric Reinhardt (University of Alabama)
• Abdulhakim Alnuqaydan (Qassim University)
• Sergei Gleyzer (University of Alabama)
• Neeraj Anand (Indian Institute of Technology Dhanbad)
• Harrison Prosper (Florida State University)
• Nobuchika Okada (University of Alabama)
• Marco Knipfer (University of Erlangen-Nürnberg)

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
• Paper 2
• Poster 1
• Blog post 1

Corresponding Project

• SYMBA

Participating Organizations

• Alabama
• FSU
• FAU
• QU
• IITDhanbad

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