Conveners
Unsupervised learning to find anomalies
- Loukas Gouskos
- Greg Landsberg
Description
We will begin with an executive summary of the current Physics Landscape at the LHC and why the approach and techniques discussed in this module have great potential to discover new physics. Next, we will provide an overview of existing approaches and techniques already been used. The students will develop an unsupervised algorithm using “known” physics processes for its training [2,3]. Then the student will check the validity of this approach utilizing "known anomalies", e.g., jets that are products of two or more merged jets due to a highly Lorentz boosted signatures. The project would provide a modular structure and the possibility of exploring different classifier architectures (e.g., convolutional vs. graph neural networks or regular vs. variational autoencoders) in parallel.
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