TY - JOUR

T1 - Deep-CEE I: Fishing for Galaxy Clusters with Deep Neural Nets

T2 - A Deep Learning Search for Galaxy Clusters

AU - Chan, Matthew C.

AU - Stott, John P.

N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version Matthew C Chan, John P Stott, Deep-CEE I: fishing for galaxy clusters with deep neural nets, Monthly Notices of the Royal Astronomical Society, Volume 490, Issue 4, December 2019, Pages 5770–5787 is available online at: https://academic.oup.com/mnras/article/490/4/5770/5601396

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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 galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm, adapted to localize and classify galaxy clusters from other astronomical objects in SDSS 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 LSST 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.

AB - 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 galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm, adapted to localize and classify galaxy clusters from other astronomical objects in SDSS 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 LSST 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.

KW - astro-ph.GA

KW - astro-ph.CO

U2 - 10.1093/mnras/stz2936

DO - 10.1093/mnras/stz2936

M3 - Journal article

VL - 490

SP - 5770

EP - 5787

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 4

ER -