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Full-Text Articles in Physical Sciences and Mathematics
Finding Strong Gravitational Lenses In The Desi Decam Legacy Survey, Xiaosheng Huang, Christopher Storfer, V. Ravi, A. Pilon, M. Domingo, D. J. Schlegel, S. Bailey, A. Dey, R. R. Gupta, D. Herrera, S. Juneau, M. Landriau, D. Lang, A. Meisner, J. Moustakas, A. D. Myers, E. F. Schlafly, F. Valdes, B. A. Weaver, J. Yang, C. Yèche
Finding Strong Gravitational Lenses In The Desi Decam Legacy Survey, Xiaosheng Huang, Christopher Storfer, V. Ravi, A. Pilon, M. Domingo, D. J. Schlegel, S. Bailey, A. Dey, R. R. Gupta, D. Herrera, S. Juneau, M. Landriau, D. Lang, A. Meisner, J. Moustakas, A. D. Myers, E. F. Schlafly, F. Valdes, B. A. Weaver, J. Yang, C. Yèche
Physics and Astronomy
We perform a semi-automated search for strong gravitational lensing systems in the 9000 deg2 Dark Energy Camera Legacy Survey (DECaLS), part of the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. The combination of the depth and breadth of these surveys are unparalleled at this time, making them particularly suitable for discovering new strong gravitational lensing systems. We adopt the deep residual neural network architecture developed by Lanusse et al. for the purpose of finding strong lenses in photometric surveys. We compile a training sample that consists of known lensing systems in the Legacy Surveys and the Dark Energy Survey as …
Discovering New Strong Gravitational Lenses In The Desi Legacy Imaging Surveys, Xiaosheng Huang, Christopher Storfer, A. Gu, V. Ravi, A. Pilon, W. Sheu, R. Venguswamy, S. Bankda, A. Dey, M. Landriau, D. Lang, A. Meisner, J. Moustakas, A. D. Myers, R. Sajith, E. F. Schlafly, D. J. Schlegel
Discovering New Strong Gravitational Lenses In The Desi Legacy Imaging Surveys, Xiaosheng Huang, Christopher Storfer, A. Gu, V. Ravi, A. Pilon, W. Sheu, R. Venguswamy, S. Bankda, A. Dey, M. Landriau, D. Lang, A. Meisner, J. Moustakas, A. D. Myers, R. Sajith, E. F. Schlafly, D. J. Schlegel
Physics and Astronomy
We have conducted a search for new strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys’ Data Release 8. We use deep residual neural networks, building on previous work presented in Huang et al. (2020). These surveys together cover approximately one third of the sky visible from the northern hemisphere, reaching a z-band AB magnitude of ∼ 22.5. We compile a training sample that consists of known lensing systems as well as non-lenses in the Legacy Surveys and the Dark Energy Survey. After applying our trained neural networks to the survey data, we visually inspect and …
Identifying Group Contributions In Nba Lineups With Spectral Analysis, Stephen Devlin, David Uminsky
Identifying Group Contributions In Nba Lineups With Spectral Analysis, Stephen Devlin, David Uminsky
Mathematics
We address the question of how to quantify the contributions of groups of players to team success. Our approach is based on spectral analysis, a technique from algebraic signal processing, which has several appealing features. First, our analysis decomposes the team success signal into components that are naturally understood as the contributions of player groups of a given size: individuals, pairs, triples, fours, and full five-player lineups. Secondly, the decomposition is orthogonal so that contributions of a player group can be thought of as pure: Contributions attributed to a group of three, for example, have been separated from the lower-order …