Home > Research > Publications & Outputs > Deep-CEE I: Fishing for Galaxy Clusters with De...

Associated organisational unit

Electronic data

  • 1906.08784

    Rights statement: 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

    Accepted author manuscript, 9.58 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Deep-CEE I: Fishing for Galaxy Clusters with Deep Neural Nets: A Deep Learning Search for Galaxy Clusters

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>1/12/2019
<mark>Journal</mark>Monthly Notices of the Royal Astronomical Society
Issue number4
Volume490
Number of pages18
Pages (from-to)5770–5787
Publication StatusPublished
<mark>Original language</mark>English

Abstract

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.

Bibliographic note

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