Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quasi Anomalous Knowledge: Searching for new physics with embedded
  knowledge   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.

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The categories of this paper are: physics
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The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: cs
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th