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HET Brown Bag | Learning New Physics from a Machine

Raffaele D'Agnolo (SLAC)

I will discuss how to use neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The algorithm that I will describe returns a global p-value that quantifies the tension between the data and the reference model. It also allows to compare directly what the network has learned with the data, giving a fully transparent account of the nature of possible signals. The potential applications are broad, from LHC physics searches to cosmology and beyond.
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When and Where

Map Randall Laboratory - 3481

February 2019

12:00pm - 1:00pm

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