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null (Ed.)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.more » « less
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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 more 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.more » « less
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Free, publicly-accessible full text available December 1, 2024
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Free, publicly-accessible full text available November 1, 2024
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Free, publicly-accessible full text available November 1, 2024
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Abstract A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015–2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.
Free, publicly-accessible full text available November 1, 2024 -
Free, publicly-accessible full text available November 1, 2024
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Free, publicly-accessible full text available November 1, 2024
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