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dc.contributor.authorFraser, T S
dc.contributor.authorTojeiro, R
dc.contributor.authorChittenden, H G
dc.identifier.citationFraser , T S , Tojeiro , R & Chittenden , H G 2023 , ' Applying unsupervised learning to resolve evolutionary histories and explore the galaxy-halo connection in IllustrisTNG ' , Monthly Notices of the Royal Astronomical Society , vol. 522 , no. 4 , stad015 .
dc.identifier.otherPURE: 283128461
dc.identifier.otherPURE UUID: 880fcef9-a7e0-41b3-b97c-91890601af9c
dc.identifier.otherJisc: 854181
dc.identifier.otherScopus: 85161574823
dc.description.abstractWe examine the effectiveness of identifying distinct evolutionary histories in IllustrisTNG-100 galaxies using unsupervised machine learning with Gaussian Mixture Models. We focus on how clustering compressed metallicity histories and star formation histories produces subpopulations of galaxies with distinct evolutionary properties (for both halo mass assembly and merger histories). By contrast, clustering with photometric colours fail to resolve such histories. We identify several populations of interest that reflect a variety of evolutionary scenarios supported by the literature. Notably, we identify a population of galaxies inhabiting the upper-red sequence, M* > 1010M⊙ that has a significantly higher ex-situ merger mass fraction present at fixed masses, and a star formation history that has yet to fully quench, in contrast to an overlapping, satellite-dominated population along the red sequence, which is distinctly quiescent. Extending the clustering to study four clusters instead of three further divides quiescent galaxies, while star forming ones are mostly contained in a single cluster, demonstrating a variety of supported pathways to quenching. In addition to these populations, we identify a handful of populations from our other clusters that are readily applicable to observational surveys, including a population related to post starburst (PSB) galaxies, allowing for possible extensions of this work in an observational context, and to corroborate results within the IllustrisTNG ecosystem.
dc.relation.ispartofMonthly Notices of the Royal Astronomical Societyen
dc.rightsCopyright © 2023 The Author(s). This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at
dc.subjectGalaxies: evolutionen
dc.subjectGalaxies: formationen
dc.subjectMethods: data analysisen
dc.subjectQB Astronomyen
dc.subjectQC Physicsen
dc.titleApplying unsupervised learning to resolve evolutionary histories and explore the galaxy-halo connection in IllustrisTNGen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen
dc.description.statusPeer revieweden

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