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Title: Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors
Authors:
; ; ;
Award ID(s):
1553281
Publication Date:
NSF-PAR ID:
10059676
Journal Name:
BMC Bioinformatics
Volume:
18
Issue:
S14
ISSN:
1471-2105
Sponsoring Org:
National Science Foundation
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  1. A bstract Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow.