Analysis of physics data using deep learning methods

Required Availability
The End of Time
Course Credit?
Yes - PH495
Paid Position?

Students can work on the following projects: 1. Novel approaches to simulation of nuclear track detector data for the MoEDAL experiment at the Large Hadron Collider using Generative Adversarial Networks. 2. Position reconstruction of gamma and beta decays in the EXO-200 neutrinoless double-beta decay experiment using Convolutional Neural Networks. Fluency in Python, experience with Linux, and familiarity with deep learning methods and software (PyTorch) are strongly preferred. Access to a GPU computing system will be provided.

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