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  1. Free, publicly-accessible full text available July 1, 2023
  2. Chromospheric Ca II activity cycles are frequently found in late-type stars, but no systematic programs have been created to search for their coronal X-ray counterparts. The typical time scale of Ca II activity cycles ranges from years to decades. Therefore, long-lasting missions are needed to detect the coronal counterparts. The XMM-Newton satellite has so far detected X-ray cycles in five stars. A particularly intriguing question is at what age (and at what activity level) X-ray cycles set in. To this end, in 2015 we started the X-ray monitoring of the young solar-like star ɛ Eridani, previously observed on two occasions:more »in 2003 and in early 2015, both by XMM-Newton . With an age of 440 Myr, it is one of the youngest solar-like stars with a known chromospheric Ca II cycle. We collected the most recent Mount Wilson S-index data available for ɛ Eridani, starting from 2002, including previously unpublished data. We found that the Ca II cycle lasts 2.92 ± 0.02 yr, in agreement with past results. From the long-term XMM-Newton lightcurve, we find clear and systematic X-ray variability of our target, consistent with the chromospheric Ca II cycle. The average X-ray luminosity is 2 × 10 28 erg s −1 , with an amplitude that is only a factor of 2 throughout the cycle. We apply a new method to describe the evolution of the coronal emission measure distribution of ɛ Eridani in terms of solar magnetic structures: active regions, cores of active regions, and flares covering the stellar surface at varying filling fractions. Combinations of these three types of magnetic structures can only describe the observed X-ray emission measure of ɛ Eridani if the solar flare emission measure distribution is restricted to events in the decay phase. The interpretation is that flares in the corona of ɛ Eridani last longer than their solar counterparts. We ascribe this to the lower metallicity of ɛ Eridani. Our analysis also revealed that the X-ray cycle of ɛ Eridani is strongly dominated by cores of active regions. The coverage fraction of cores throughout the cycle changes by the same factor as the X-ray luminosity. The maxima of the cycle are characterized by a high percentage of covering fraction of the flares, consistent with the fact that flaring events are seen in the corresponding short-term X-ray lightcurves predominately at the cycle maxima. The high X-ray emission throughout the cycle of ɛ Eridani is thus explained by the high percentage of magnetic structures on its surface.« less
  3. The purpose of the project is to identify how to measure various types of institutional support as it pertains to underrepresented and underserved populations in colleges of engineering and science. We are grounding this investigation in the Model of Co-Curricular Support, a conceptual framework that emphasizes the breadth of assistance currently used to support undergraduate students in engineering and science. The results from our study will help prioritize the elements of institutional support that should appear somewhere in a college’s suite of support efforts to improve engineering and science learning environments and design effective programs, activities, and services. Our postermore »will present: 1) an overview of the instrument development process; 2) evaluation of the prototype for face and content validity from students and experts; and 3) instrument revision and data collection to determine test validity and reliability across varied institutional contexts. In evaluating the initial survey, we included multiple rounds of feedback from students and experts, receiving feedback from 46 participants (38 students, 8 administrators). We intentionally sampled for representation across engineering and science colleges; gender identity; race/ethnicity; international student status; and transfer student status. The instrument was deployed for the first time in Spring 2018 to the institutional project partners at three universities. It was completed by 722 students: 598 from University 1, 51 from University 2, and 123 from University 3. We tested the construct validity of these responses using a minimum residuals exploratory factor analysis and correlation. A preliminary data analysis shows evidence of differences in perception on types of support college of engineering and college of science students experience. The findings of this preliminary analysis were used to revise the instrument further prior to the next round of testing. Our target sample for the next instrument deployment is 2,000 students, so we will survey ~13,000 students based on a 15% anticipated response rate. Following data collection, we will use confirmatory factor analysis to continue establishing construct validity and report on the stability of constructs emerging from our piloting on a new student sample(s). We will also investigate differences across these constructs by subpopulations of students.« less
  4. 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
  5. 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
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  7. Free, publicly-accessible full text available May 1, 2023