Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System
The Vera C. Rubin Observatory will, over a period of 10 yr, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an active-optics system (AOS) to correct for alignment and mirror surface perturbatio...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/ad1661 |
| _version_ | 1797322737550622720 |
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| author | John Franklin Crenshaw Andrew J. Connolly Joshua E. Meyers J. Bryce Kalmbach Guillem Megias Homar Tiago Ribeiro Krzysztof Suberlak Sandrine Thomas Te-Wei Tsai |
| author_facet | John Franklin Crenshaw Andrew J. Connolly Joshua E. Meyers J. Bryce Kalmbach Guillem Megias Homar Tiago Ribeiro Krzysztof Suberlak Sandrine Thomas Te-Wei Tsai |
| author_sort | John Franklin Crenshaw |
| collection | DOAJ |
| description | The Vera C. Rubin Observatory will, over a period of 10 yr, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an active-optics system (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this, Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wave front. We have designed and integrated a deep-learning (DL) model for wave-front estimation into the AOS pipeline. In this paper, we compare the performance of this DL approach to Rubin’s baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the DL approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the DL model is 40× faster, the median error 2× better under ideal conditions, 5× better in the presence of vignetting by the Rubin camera, and 14× better in the presence of blending in crowded fields. In addition, the DL model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin. |
| first_indexed | 2024-03-08T05:17:39Z |
| format | Article |
| id | doaj.art-0670ae2c79e8418197b6410e2f3c1d54 |
| institution | Directory Open Access Journal |
| issn | 1538-3881 |
| language | English |
| last_indexed | 2024-03-08T05:17:39Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj.art-0670ae2c79e8418197b6410e2f3c1d542024-02-06T14:28:22ZengIOP PublishingThe Astronomical Journal1538-38812024-01-0116728610.3847/1538-3881/ad1661Using AI for Wave-front Estimation with the Rubin Observatory Active Optics SystemJohn Franklin Crenshaw0https://orcid.org/0000-0002-2495-3514Andrew J. Connolly1https://orcid.org/0000-0001-5576-8189Joshua E. Meyers2https://orcid.org/0000-0002-2308-4230J. Bryce Kalmbach3https://orcid.org/0000-0002-6825-5283Guillem Megias Homar4https://orcid.org/0000-0001-6013-1131Tiago Ribeiro5https://orcid.org/0000-0002-0138-1365Krzysztof Suberlak6https://orcid.org/0000-0002-9589-1306Sandrine Thomas7https://orcid.org/0000-0002-9121-3436Te-Wei Tsai8https://orcid.org/0009-0007-5732-4160Department of Physics, University of Washington , Seattle, WA 98195, USA ; jfc20@uw.edu; DIRAC Institute, University of Washington , Seattle, WA 98195, USADIRAC Institute, University of Washington , Seattle, WA 98195, USA; Department of Astronomy, University of Washington , Seattle, WA 98195, USA; eScience Institute, University of Washington , Seattle, WA 98195, USAKavli Institute for Particle Astrophysics and Cosmology , Stanford, CA 94305, USA; SLAC National Accelerator Laboratory , Menlo Park, CA 94025, USADIRAC Institute, University of Washington , Seattle, WA 98195, USA; Department of Astronomy, University of Washington , Seattle, WA 98195, USAKavli Institute for Particle Astrophysics and Cosmology , Stanford, CA 94305, USA; SLAC National Accelerator Laboratory , Menlo Park, CA 94025, USA; Department of Aeronautics and Astronautics, Stanford University , Stanford, CA 94305, USAVera C. Rubin Observatory , Tucson, AZ 85719, USADIRAC Institute, University of Washington , Seattle, WA 98195, USA; Department of Astronomy, University of Washington , Seattle, WA 98195, USAVera C. Rubin Observatory , Tucson, AZ 85719, USAVera C. Rubin Observatory , Tucson, AZ 85719, USAThe Vera C. Rubin Observatory will, over a period of 10 yr, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an active-optics system (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this, Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wave front. We have designed and integrated a deep-learning (DL) model for wave-front estimation into the AOS pipeline. In this paper, we compare the performance of this DL approach to Rubin’s baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the DL approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the DL model is 40× faster, the median error 2× better under ideal conditions, 5× better in the presence of vignetting by the Rubin camera, and 14× better in the presence of blending in crowded fields. In addition, the DL model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.https://doi.org/10.3847/1538-3881/ad1661Neural networksMultiple mirror telescopesOptical telescopesAstronomical opticsAstronomical instrumentation |
| spellingShingle | John Franklin Crenshaw Andrew J. Connolly Joshua E. Meyers J. Bryce Kalmbach Guillem Megias Homar Tiago Ribeiro Krzysztof Suberlak Sandrine Thomas Te-Wei Tsai Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System The Astronomical Journal Neural networks Multiple mirror telescopes Optical telescopes Astronomical optics Astronomical instrumentation |
| title | Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System |
| title_full | Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System |
| title_fullStr | Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System |
| title_full_unstemmed | Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System |
| title_short | Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System |
| title_sort | using ai for wave front estimation with the rubin observatory active optics system |
| topic | Neural networks Multiple mirror telescopes Optical telescopes Astronomical optics Astronomical instrumentation |
| url | https://doi.org/10.3847/1538-3881/ad1661 |
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