Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies
| Autor: | Mike Walmsley, Chris Lintott, Tobias Géron, Sandor Kruk, Coleman Krawczyk, Kyle W Willett, Steven Bamford, Lee S Kelvin, Lucy Fortson, Yarin Gal, William Keel, Karen L Masters, Vihang Mehta, Brooke D Simmons, Rebecca Smethurst, Lewis Smith, Elisabeth M Baeten, Christine Macmillan |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition FOS: Physical sciences Astrophysics Astrophysics::Cosmology and Extragalactic Astrophysics 01 natural sciences Convolutional neural network Footprint bar [galaxies] 0103 physical sciences data analysis [methods] 010303 astronomy & astrophysics Astrophysics::Galaxy Astrophysics Physics Spiral galaxy 010308 nuclear & particles physics business.industry interactions [galaxies] Deep learning Astronomy and Astrophysics Astrophysics - Astrophysics of Galaxies Galaxy Space and Planetary Science Feature (computer vision) Astrophysics of Galaxies (astro-ph.GA) Artificial intelligence business general [galaxies] |
| Zdroj: | Walmsley, M, Lintott, C J, Géron, T, Kruk, S, Krawczyk, C, Willett, K W, Bamford, S, Kelvin, L S, Fortson, L, Gal, Y, Keel, W, Masters, K L, Mehta, V, Simmons, B D, Smethurst, R, Smith, L, Baeten, E M & Macmillan, C 2022, ' Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies ', Monthly Notices of the Royal Astronomical Society, vol. 509, no. 3, pp. 3966-3988 . https://doi.org/10.1093/mnras/stab2093 |
| DOI: | 10.1093/mnras/stab2093 |
| Popis: | We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r=23.6 vs. r=22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314,000 galaxies. 140,000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314,000 galaxies. When measured against confident volunteer classifications, the networks are approximately 99% accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve. Accepted by MNRAS July '21. Open access DOI below. Data at https://doi.org/10.5281/zenodo.4196266. Code at https://www.github.com/mwalmsley/zoobot. Docs at https://zoobot.readthedocs.io/. Interactive viewer at https://share.streamlit.io/mwalmsley/galaxy-poster/gz_decals_mike_walmsley.py |
| Databáze: | OpenAIRE |
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