Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning
Sebastian Turner, Ma{\l}gorzata Siudek, Samir Salim, Ivan K. Baldry,, Agnieszka Pollo, Steven N. Longmore, Katarzyna Ma{\l}ek, Chris A. Collins,, Paulo J. Lisboa, Janusz Krywult, Thibaud Moutard, Daniela Vergani, and, Alexander Fritz

TL;DR
This study uses unsupervised machine learning to analyze galaxy color data at two different cosmic epochs, revealing substructures within galaxy populations and insights into their evolutionary processes.
Contribution
It introduces a clustering approach that identifies galaxy subpopulations across redshifts using multi-dimensional color data, highlighting evolutionary differences.
Findings
Seven galaxy clusters identified at both epochs.
Distinct evolutionary pathways for star-forming and passive galaxies.
Environmental effects influence low-mass passive galaxy evolution at low redshift.
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 multi-dimensional 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; ), and the other from the VIMOS Public Extragalactic Redshift Survey (VIPERS; ). We cluster within a nine-dimensional feature space defined purely by…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
