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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 …
Finding Strong Gravitational Lenses With Residual Neural Networks, Christopher Storfer, Andrew Pilon, Varun Ravi, Matthew Domingo
Finding Strong Gravitational Lenses With Residual Neural Networks, Christopher Storfer, Andrew Pilon, Varun Ravi, Matthew Domingo
Creative Activity and Research Day - CARD
Measuring gravitational lensing by galaxies is the only way to directly study the elusive dark matter. However, gravitational lensing is a very rare phenomenon (~1 in 10,000 galaxies). Our goal is to find new strong gravitational lenses using deep neural networks (“neural nets”). We train our neural nets on a hand-labeled set of images, consisting of both lenses and non-lenses (“the training sample”). We then apply the trained neural nets to a “validation set” to assess the accuracy and precision of its predictions. Given the rarity of lenses, we cannot tolerate a false positive rate higher than 0.1%. This is …
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 …