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  1. Free, publicly-accessible full text available February 1, 2023
  2. Free, publicly-accessible full text available February 1, 2023
  3. Despite considerable social scientific attention to the impacts of the COVID-19 pandemic on urbanized areas, very little research has examined its impact on rural populations. Yet rural communities—which make up tens of millions of people from diverse backgrounds in the United States—are among the nation’s most vulnerable populations and may be less resilient to the effects of such a large-scale exogenous shock. We address this critical knowledge gap with data from a new survey designed to assess the impacts of the pandemic on health-related and economic dimensions of rural well-being in the North American West. Notably, we find that themore »effects of the COVID-19 pandemic on rural populations have been severe, with significant negative impacts on unemployment, overall life satisfaction, mental health, and economic outlook. Further, we find that these impacts have been generally consistent across age, ethnicity, education, and sex. We discuss how these findings constitute the beginning of a much larger interdisciplinary COVID-19 research effort that integrates rural areas and pushes beyond the predominant focus on cities and nation-states.

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  4. Abstract The Phase-I trigger readout electronics upgrade of the ATLAS Liquid Argon calorimeters enhances thephysics reach of the experiment during the upcoming operation atincreasing Large Hadron Collider luminosities.The new system, installed during the second Large Hadron Collider Long Shutdown,increases the trigger readout granularity by up to a factor of tenas well as its precision and range.Consequently, the background rejection at trigger level is improvedthrough enhanced filtering algorithms utilizing the additional informationfor topological discrimination of electromagnetic and hadronic shower shapes.This paper presents the final designs of the new electronic elements,their custom electronic devices, the proceduresused to validate their proper functioning, andmore »the performance achievedduring the commissioning of this system.« less
    Free, publicly-accessible full text available May 1, 2023
  5. 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
  6. Abstract The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed tomore »meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.« less
    Free, publicly-accessible full text available December 1, 2023
  7. Free, publicly-accessible full text available April 1, 2023