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  1. As technology advances, data driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high frequency monitoring data collected frommore »real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings has improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses.« less
    Free, publicly-accessible full text available July 26, 2022
  2. Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hardmore »scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.« less
    Free, publicly-accessible full text available December 1, 2023
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  5. Abstract The energy response of the ATLAS calorimeter is measured for single charged pions with transverse momentum in the range $$10more »situ single-particle measurements. The calorimeter response to single-pions is observed to be overestimated by $${\sim }2\%$$ ∼ 2 % across a large part of the $$p_{\text {T}}$$ p T spectrum in the central region and underestimated by $${\sim }4\%$$ ∼ 4 % in the endcaps in the ATLAS simulation. The uncertainties in the measurements are $${\lesssim }1\%$$ ≲ 1 % for $$15« less
    Free, publicly-accessible full text available March 1, 2023
  6. Abstract This paper presents a measurement of the electroweak production of two jets in association with a $$Z\gamma $$ Z γ pair, with the Z boson decaying into two neutrinos. It also presents a search for invisible or partially invisible decays of a Higgs boson with a mass of 125  $$\text {GeV}$$ GeV produced through vector-boson fusion with a photon in the final state. These results use data from LHC proton–proton collisions at $$\sqrt{s}$$ s = 13  $$\text {TeV}$$ TeV collected with the ATLAS detector and corresponding to an integrated luminosity of 139  $$\hbox {fb}^{-1}$$ fb - 1 . Themore »event signature, shared by all benchmark processes considered for the measurements and searches, is characterized by a significant amount of unbalanced transverse momentum and a photon in the final state, in addition to a pair of forward jets. Electroweak $$Z\gamma $$ Z γ production in association with two jets is observed in this final state with a significance of 5.2 (5.1 expected) standard deviations. The measured fiducial cross-section for this process is $$1.31\pm 0.29$$ 1.31 ± 0.29  fb. An observed (expected) upper limit of 0.37 ( $$0.34^{+0.15}_{-0.10}$$ 0 . 34 - 0.10 + 0.15 ) at 95% confidence level is set on the branching ratio of a 125  $$\text {GeV}$$ GeV Higgs boson to invisible particles, assuming the Standard Model production cross-section. The signature is also interpreted in the context of decays of a Higgs boson into a photon and a dark photon. An observed (expected) 95% CL upper limit on the branching ratio for this decay is set at 0.018 ( $$0.017^{+0.007}_{-0.005}$$ 0 . 017 - 0.005 + 0.007 ), assuming the Standard Model production cross-section for a 125  $$\text {GeV}$$ GeV Higgs boson.« less
    Free, publicly-accessible full text available February 1, 2023
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  8. Abstract During LHC Run 2 (2015–2018) the ATLAS Level-1 topological trigger allowed efficient data-taking by the ATLAS experiment at luminosities up to 2.1 $$\times $$ × 10 $$^{34}$$ 34  cm $$^{-2}$$ - 2 s $$^{-1}$$ - 1 , which exceeds the design value by a factor of two. The system was installed in 2016 and operated in 2017 and 2018. It uses Field Programmable Gate Array processors to select interesting events by placing kinematic and angular requirements on electromagnetic clusters, jets, $$\tau $$ τ -leptons, muons and the missing transverse energy. It allowed to significantly improve the background event rejection andmore »signal event acceptance, in particular for Higgs and B -physics processes.« less
    Free, publicly-accessible full text available January 1, 2023
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