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HET Brown Bag | Learning New Physics from a Machine

Raffaele D'Agnolo (SLAC)
Wednesday, February 13, 2019
12:00-1:00 PM
3481 Randall Laboratory Map
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.
Building: Randall Laboratory
Event Type: Workshop / Seminar
Tags: Faculty, Free, Graduate, Graduate Students, Lecture, Natural Sciences, Physics, Science, Talk, Undergraduate, Undergraduate Students
Source: Happening @ Michigan from HET Brown Bag Series, Department of Physics