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. In order to correctly reconstruct particles of interest, high resolution is required.

This project will focus on developing machine learning models to map processed, lower resolution data from particle from simulated particle collisions back to a higher resolution representation.

Duration

Total project length: 175/350 hours.

Task ideas

• Develop a machine-learning super-resolution model to upsample particle collision data
• Analyze performance on ground-truth simulated higher resolution data

Expected results

• Trained models and benchmarks on simulated data

Test

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

Requirements

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

Mentors

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

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