Deep-CEE I: Fishing for Galaxy Clusters with Deep Neural Nets
Jun 20, 201918 pages
Published in:
- Mon.Not.Roy.Astron.Soc. 490 (2019) 4, 5770-5787
- Published: Dec 21, 2019
e-Print:
- 1906.08784 [astro-ph.GA]
DOI:
- 10.1093/mnras/stz2936 (publication)
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Abstract: (Oxford University Press)
We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for photometric catalogues. This technique is complementary to traditional methods and could also be used in combination with them to confirm existing galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm, adapted to localize and classify galaxy clusters from other astronomical objects in Sloan Digital Sky Survey imaging. As there is an abundance of labelled data for galaxy clusters from previous classifications in publicly available catalogues, we do not need to rely on simulated data. This means we keep our training data as realistic as possible, which is advantageous when training a deep learning algorithm. Ultimately, we will apply our model to surveys such as Large Synoptic Survey Telescope and Euclid to probe wider and deeper into unexplored regions of the Universe. This will produce large samples of both high-redshift and low-mass clusters, which can be utilized to constrain both environment-driven galaxy evolution and cosmology.Note:
- Submitted to MNRAS on 20th June 2019, 19 pages; Minor changes with grammar/spelling/LaTeX in paper, added a reference in section 1, minor updates to text/figures in section 2 and 3, overall results unchanged
- methods: data analysis
- methods: statistical
- techniques: image processing
- galaxies: clusters: general
References(108)
Figures(30)