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  1. ; ; ; ; ; ; ; ; ; ( , The astrophysical journal)
    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 »levels by exposing a solid gold target to plasma in the Compact Toroidal Hybrid (CTH) experiment at Auburn University. A wavelength range from 187 to 800 nm was studied. In Au I, 86 lines are observed, 43 of which are unreported in the literature, and the energies of 18 5d9 6s 6p levels and 16 of the 18 known 5d9 6s 6d levels are corroborated by a least-squares level energy optimization. In Au II, 76 emission lines are observed, and 51 of the lines are unreported in the literature. For both Au I and Au II, the new lines predominantly originate from the most energetic of the known levels, and over half of the new Au II lines have wavelengths longer than 300 nm. For the estimated electron parameters of CTH plasmas at the gold target (ne~10^12 cm−3, Te~10 eV), two-electron transitions are similar in intensity to LS-allowed one-electron transitions.« less
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  2. ; ; ; ; ; ; ( , The Astronomical Journal)
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  3. ; ; ; ; ; ; ; ; ; ; et al ( , Computing and Software for Big Science)
    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
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Computing and Software for Big Science)
    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
  5. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal)
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  6. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal)
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  7. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal)
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  8. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
    Free, publicly-accessible full text available May 1, 2023
  9. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
    Free, publicly-accessible full text available May 1, 2023
  10. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal)
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