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 |
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Website: | |
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 |