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  1. 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.more »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.« less
    Free, publicly-accessible full text available June 4, 2022
  2. Langran, E. ; Archambault, L. (Ed.)
    Improving the quantity and quality of STEM teachers is one of the goals of the Noyce Scholarship Grant Program implemented through the National Science Foundation. Here, we examine how the change to remote learning impacted ASU'’s Noyce Scholarship program. Our research question was as follows: what kinds of successes and struggles did STEM Noyce (a) in-service and (b) pre-service scholars experience in the first year of remote teaching during the pandemic? We interviewed three different cohorts of Noyce scholars: preservice teachers, first year teachers and second year teachers. Results showed that some scholars saw benefits to remote learning including moremore »free time for other learning activities. They also observed teachers dedicated to student success. Challenges included an inability to reach all students, a continued focus on standardized tests, and a lack of flexibility within the districts.« less
  3. Free, publicly-accessible full text available March 1, 2023
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  5. A bstract A search is presented for new particles produced at the LHC in proton-proton collisions at $$ \sqrt{s} $$ s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb − 1 , collected in 2017–2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with anmore »earlier search based on a data sample of 36 fb − 1 , collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.« less
    Free, publicly-accessible full text available November 1, 2022
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