Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Construct Validity of the Profile of Mood States

Objectives: The purpose of the present study was to extend the validation of the Profile of Mood States-Adolescents (POMS-A: Terry, P. C., Lane, A. M., Lane, H. J., & Keohane, L. (1999). Development and validation of a mood measure for adolescents. Journal of Sports Sciences, 17, 861-872) from adolescent to adult populations. Design: A strategy of assessing the invariance of the POMS-A factor structure among disparate samples and of testing relationships with concurrent measures was used. Methods: The POMS-A was administered to 2,549 participants from four samples: Adult athletes prior to competition (n = 621), adult student athletes in a classroom (n = 656), adolescent athletes prior to competition (n = 676), and adolescent students in a classroom (n = 596). A subset of 382 adult student athletes was used to test the criterion validity of the POMS-A. Results: Confirmatory factor analysis provided support for the factorial validity of a 24-item, six-factor model using both independent and multi-sample analyses. Relationships between POMS-A scores and previously validated measures, that were consistent with theoretical predictions, supported criterion validity. Conclusion: Supporting evidence was found that the psychometric integrity of the POMS-A extended from adolescent to adult populations