A1 Journal article (refereed)
Euclid preparation : XXII. Selection of quiescent galaxies from mock photometry using machine learning (2023)
Euclid Collaboration. (2023). Euclid preparation : XXII. Selection of quiescent galaxies from mock photometry using machine learning. Astronomy and Astrophysics, 671, Article A99. https://doi.org/10.1051/0004-6361/202244307
JYU authors or editors
Publication details
All authors or editors: Euclid Collaboration
Journal or series: Astronomy and Astrophysics
ISSN: 0004-6361
eISSN: 1432-0746
Publication year: 2023
Publication date: 14/03/2023
Volume: 671
Article number: A99
Publisher: EDP Sciences
Publication country: France
Publication language: English
DOI: https://doi.org/10.1051/0004-6361/202244307
Publication open access: Openly available
Publication channel open access: Partially open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/86250
Publication is parallel published: https://arxiv.org/abs/2209.13074
Abstract
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2 of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid IE, YE, JE, and HE photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has been designed to have 'sparsity awareness', such that missing photometry values are informative for the classification. In addition, our pipeline is able to derive photometric redshifts for galaxies selected as quiescent, aided by the 'pseudo-labelling' semi-supervised method, and using an outlier detection algorithm to identify and reject likely catastrophic outliers. After the application of the outlier filter, our pipeline achieves a normalised mean absolute deviation of ≲0.03 and a fraction of catastrophic outliers of ≲0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey photometry with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey photometry with ancillary ugriz, WISE, and radio data; and (iii) Euclid Wide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperforms UVJ selection, in addition to the Euclid IE – YE, JE – HE and u – IE, IE – JE colour-colour methods, with improvements in completeness and the F1-score (the harmonic mean of precision and recall) of up to a factor of 2.
Keywords: galaxies; photometry; classification; machine learning
Free keywords: galaxies; photometry; high-redshift; evolution; general methods; statistical
Contributing organizations
Ministry reporting: Yes
VIRTA submission year: 2023
JUFO rating: 3