Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning. Issue 2 (8th March 2021)
- Record Type:
- Journal Article
- Title:
- Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning. Issue 2 (8th March 2021)
- Main Title:
- Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning
- Authors:
- Turner, Sebastian
Siudek, Malgorzata
Salim, Samir
Baldry, Ivan K
Pollo, Agnieszka
Longmore, Steven N
Malek, Katarzyna
Collins, Chris A
Lisboa, Paulo J
Krywult, Janusz
Moutard, Thibaud
Vergani, Daniela
Fritz, Alexander - Abstract:
- ABSTRACT: The colour bimodality of galaxies provides an empirical basis for theories of galaxy evolution. However, the balance of processes that begets this bimodality has not yet been constrained. A more detailed view of the galaxy population is needed, which we achieve in this paper by using unsupervised machine learning to combine multidimensional data at two different epochs. We aim to understand the cosmic evolution of galaxy subpopulations by uncovering substructures within the colour bimodality. We choose a clustering algorithm that models clusters using only the most discriminative data available, and apply it to two galaxy samples: one from the second edition of the GALEX-SDSS-WISE Legacy Catalogue (GSWLC-2; z ∼ 0.06), and the other from the VIMOS Public Extragalactic Redshift Survey (VIPERS; z ∼ 0.65). We cluster within a nine-dimensional feature space defined purely by rest-frame ultraviolet-through-near-infrared colours. Both samples are similarly partitioned into seven clusters, breaking down into four of mostly star-forming galaxies (including the vast majority of green valley galaxies) and three of mostly passive galaxies. The separation between these two families of clusters suggests differences in the evolution of their galaxies, and that these differences are strongly expressed in their colours alone. The samples are closely related, with star-forming/green-valley clusters at both epochs forming morphological sequences, capturing the gradual internallyABSTRACT: The colour bimodality of galaxies provides an empirical basis for theories of galaxy evolution. However, the balance of processes that begets this bimodality has not yet been constrained. A more detailed view of the galaxy population is needed, which we achieve in this paper by using unsupervised machine learning to combine multidimensional data at two different epochs. We aim to understand the cosmic evolution of galaxy subpopulations by uncovering substructures within the colour bimodality. We choose a clustering algorithm that models clusters using only the most discriminative data available, and apply it to two galaxy samples: one from the second edition of the GALEX-SDSS-WISE Legacy Catalogue (GSWLC-2; z ∼ 0.06), and the other from the VIMOS Public Extragalactic Redshift Survey (VIPERS; z ∼ 0.65). We cluster within a nine-dimensional feature space defined purely by rest-frame ultraviolet-through-near-infrared colours. Both samples are similarly partitioned into seven clusters, breaking down into four of mostly star-forming galaxies (including the vast majority of green valley galaxies) and three of mostly passive galaxies. The separation between these two families of clusters suggests differences in the evolution of their galaxies, and that these differences are strongly expressed in their colours alone. The samples are closely related, with star-forming/green-valley clusters at both epochs forming morphological sequences, capturing the gradual internally driven growth of galaxy bulges. At high stellar masses, this growth is linked with quenching. However, it is only in our low-redshift sample that additional, environmental processes appear to be involved in the evolution of low-mass passive galaxies. … (more)
- Is Part Of:
- Monthly notices of the Royal Astronomical Society. Volume 503:Issue 2(2021)
- Journal:
- Monthly notices of the Royal Astronomical Society
- Issue:
- Volume 503:Issue 2(2021)
- Issue Display:
- Volume 503, Issue 2 (2021)
- Year:
- 2021
- Volume:
- 503
- Issue:
- 2
- Issue Sort Value:
- 2021-0503-0002-0000
- Page Start:
- 3010
- Page End:
- 3031
- Publication Date:
- 2021-03-08
- Subjects:
- methods: statistical -- galaxies: evolution -- galaxies: general -- galaxies: star formation -- galaxies: statistics -- galaxies: stellar content
Astronomy -- Periodicals
Periodicals
520.5 - Journal URLs:
- http://mnras.oxfordjournals.org/ ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2966 ↗
http://www.blackwell-synergy.com/issuelist.asp?journal=mnr ↗
http://www.blackwell-synergy.com/loi/mnr ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/mnras/stab653 ↗
- Languages:
- English
- ISSNs:
- 0035-8711
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5943.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 27102.xml