Deep Learning Versatility in New Physics Searches
February 02, 2021
In this communication, we will discuss the application of Deep Learning models as a discriminant step to improve sensitivity at searches for new physics. Of particular interest, we will focus on the transferability of Deep Learning models, where a neural network trained to isolate a specific signal can still provide sensitivity when deriving upper limits on a different process. This is expanded to include a discussion on the versatility of Deep Learning models to provide enough sensitivity in cases where the signal present in the sample does not follow the assumptions of an analysis.
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