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The recent detection of a neutron star merger by the LIGO collaboration has renewed interest in laboratory studies of r-process elements. Accurate modeling and interpretation of the electromagnetic transients following the mergers requires computationally expensive calculations of both the structure and opacity of all trans-iron elements. To date, the necessary atomic data to benchmark structure codes are incomplete or, in some cases, absent entirely. Within the available laboratory studies, the literature on Au I and Au II provides incomplete reports of the emission lines and level structures. We present a new study of Au I and Au II lines andmore »
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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 »Free, publicly-accessible full text available December 1, 2023
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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 »Free, publicly-accessible full text available December 1, 2023
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Free, publicly-accessible full text available May 1, 2023
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Free, publicly-accessible full text available May 1, 2023