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 (matrix M).

Duration

Total project length: 175/350 hours.

Task ideas and expected results

• Explore physics-informed ideas for improving data representations, physics-aware models, and physics simulations for squared amplitude calculation
• Apply symbolic machine learning techniques to predict the squared amplitudes and cross section for high-energy physics

Requirements

• Python, C++ and some experience in Machine Learning sequence models.
• Knowledge of physics and linear algebra is desired

Difficulty Level

Intermediate/Advanced

Mentors

• Abdulhakim Alnuqaydan (Qassim University)
• Sergei Gleyzer (University of Alabama)
• Harrison Prosper (Florida State University)
• Eric Reinhardt (University of Alabama)

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
• Paper 3

Corresponding Project

• SYMBA

Participating Organizations

• Alabama
• FSU
• QU

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