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. In this project we will explore state-space models (SSMs) to map from amplitudes to squared amplitudes using sequence to sequence representations.

Duration

Total project length: 175/350 hours.

Task ideas and expected results

• Develop and deploy state-space models (SSM) for symbolic solutions of squared amplitude calculations
• Benchmark the SSM models with other transformer-based solutions
• Integrated with the SymbaHEP pipeline

Requirements

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

Difficulty Level

Advanced

Test

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

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. 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
• Paper 3
• Poster 1
• Blog post 1

Corresponding Project

• SYMBA

Participating Organizations

• Alabama
• FSU
• FAU
• QU
• IITDhanbad

---