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Semi-Supervised Symmetry Discovery in CMS Data by John is a document available to read on EtoBox.

What is Semi-Supervised Symmetry Discovery in CMS Data about?

This GSOC proposal outlines a project aimed at discovering hidden symmetries in CMS calorimeter data using a semi-supervised deep learning approach with Variational Autoencoders (VAEs). The project will develop a pipeline for data preprocessing, symmetry discovery, and benchmarking against known symmetries, with deliverables including a Python codebase, visualizations, and a final report. The methodology focuses on leveraging physics observables as oracles to guide the learning process and improve model int

Author

John

Language

EN