Euclid preparation, XXII. Selection of quiescent galaxies from mock photometry using machine learning Article Swipe
A. Humphrey
,
L. Bisigello
,
P. A. C. Cunha
,
M. Bolzonella
,
S. Fotopoulou
,
K. I. Caputi
,
C. Tortora
,
G. Zamorani
,
P. Papaderos
,
D. Vergani
,
J. Brinchmann
,
M. Moresco
,
A. Amara
,
N. Auricchio
,
M Baldi
,
R. Bender
,
D. Bonino
,
E. Branchini
,
M. Brescia
,
S. Camera
,
V. Capobianco
,
C. Carbone
,
J. Carretero
,
F. J. Castander
,
M. Castellano
,
S. Cavuoti
,
A. Cimatti
,
R. Cledassou
,
G. Congedo
,
Christopher J. Conselice
,
L. Conversi
,
Y. Copin
,
L. Corcione
,
F. Courbin
,
M. Cropper
,
A. Da Silva
,
H. Degaudenzi
,
M. Douspis
,
F. Dubath
,
C. A. J. Duncan
,
X. Dupac
,
S Dusini
,
S. Farrens
,
S. Ferriol
,
M. Frailis
,
E. Franceschi
,
M Fumana
,
P. G'omez-Alvarez
,
S. Galeotta
,
B. Garilli
,
W. Gillard
,
B. Gillis
,
C. Giocoli
,
A. Grazian
,
F Grupp
,
L. Guzzo
,
S. V. H. Haugan
,
W Holmes
,
F. Hormuth
,
K. Jahnkę
,
M. Kummel
,
S. Kermiche
,
A. Kiessling
,
M. Kilbinger
,
T. Kitching
,
R. Kohley
,
M. Kunz
,
H. Kurki‐Suonio
,
S. Ligori
,
P. B. Lilje
,
I Lloro
,
E. Maiorano
,
O. Mansutti
,
O. Marggraf
,
K. Markovič
,
F. Marulli
,
R. Massey
,
S. Maurogordato
,
H. J. McCracken
,
E. Medinaceli
,
M. Melchior
,
M. Meneghetti
,
E. Merlin
,
G Meylan
,
L Moscardini
,
E. Munari
,
R. Nakajima
,
S. M. Niemi
,
J.W Nightingale
,
C. Padilla
,
S. Paltani
,
F. Pasian
,
K. Pedersen
,
V. Pettorino
,
S. Pires
,
M Poncet
,
L. Popa
,
L. Pozzetti
,
F. Raison
,
A. Renzi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18154/rwth-2023-08626
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18154/rwth-2023-08626
The Euclid Space Telescope will provide deep imaging at optical and\nnear-infrared wavelengths, along with slitless near-infrared spectroscopy,\nacross ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion\nastronomical sources, facilitating new insights into cosmology, galaxy\nevolution, and various other topics. To optimally exploit the expected very\nlarge data set, there is the need to develop appropriate methods and software.\nHere we present a novel machine-learning based methodology for selection of\nquiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in\ncombination with multiwavelength photometry from other surveys. The ARIADNE\npipeline uses meta-learning to fuse decision-tree ensembles,\nnearest-neighbours, and deep-learning methods into a single classifier that\nyields significantly higher accuracy than any of the individual learning\nmethods separately. The pipeline has `sparsity-awareness', so that missing\nphotometry values are still informative for the classification. Our pipeline\nderives photometric redshifts for galaxies selected as quiescent, aided by the\n`pseudo-labelling' semi-supervised method. After application of the outlier\nfilter, our pipeline achieves a normalized mean absolute deviation of ~< 0.03\nand a fraction of catastrophic outliers of ~< 0.02 when measured against the\nCOSMOS2015 photometric redshifts. We apply our classification pipeline to mock\ngalaxy photometry catalogues corresponding to three main scenarios: (i) Euclid\nDeep Survey with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey\nwith ancillary ugriz, WISE, and radio data; (iii) Euclid Wide Survey only. Our\nclassification pipeline outperforms UVJ selection, in addition to the Euclid\nI_E-Y_E, J_E-H_E and u-I_E,I_E-J_E colour-colour methods, with improvements in\ncompleteness and the F1-score of up to a factor of 2. (Abridged)\n
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2209.13074
- https://arxiv.org/pdf/2209.13074
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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- DOI
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Euclid preparation, XXII. Selection of quiescent galaxies from mock photometry using machine learningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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A. Humphrey, L. Bisigello, P. A. C. Cunha, M. Bolzonella, S. Fotopoulou, K. I. Caputi, C. Tortora, G. Zamorani, P. Papaderos, D. Vergani, J. Brinchmann, M. Moresco, A. Amara, N. Auricchio, M Baldi, R. Bender, D. Bonino, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, Christopher J. Conselice, L. Conversi, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, M Fumana, P. G'omez-Alvarez, S. Galeotta, B. Garilli, W. Gillard, B. Gillis, C. Giocoli, A. Grazian, F Grupp, L. Guzzo, S. V. H. Haugan, W Holmes, F. Hormuth, K. Jahnkę, M. Kummel, S. Kermiche, A. Kiessling, M. Kilbinger, T. Kitching, R. Kohley, M. Kunz, H. Kurki‐Suonio, S. Ligori, P. B. Lilje, I Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovič, F. Marulli, R. Massey, S. Maurogordato, H. J. McCracken, E. Medinaceli, M. Melchior, M. Meneghetti, E. Merlin, G Meylan, L Moscardini, E. Munari, R. Nakajima, S. M. Niemi, J.W Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, M Poncet, L. Popa, L. Pozzetti, F. Raison, A. RenziList of authors in order
- Landing page
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https://arxiv.org/abs/2209.13074Publisher landing page
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https://arxiv.org/pdf/2209.13074Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2209.13074Direct OA link when available
- Concepts
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Photometry (optics), Selection (genetic algorithm), Galaxy, Computer science, Artificial intelligence, Astronomy, Astrophysics, Physics, StarsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.deviation | 151 |
| abstract_inverted_index.optimally | 42 |
| abstract_inverted_index.redshifts | 128 |
| abstract_inverted_index.selection | 67 |
| abstract_inverted_index.broad-band | 71 |
| abstract_inverted_index.catalogues | 177 |
| abstract_inverted_index.classifier | 99 |
| abstract_inverted_index.cosmology, | 35 |
| abstract_inverted_index.individual | 108 |
| abstract_inverted_index.normalized | 148 |
| abstract_inverted_index.photometry | 81, 176 |
| abstract_inverted_index.quiescent, | 133 |
| abstract_inverted_index.redshifts. | 168 |
| abstract_inverted_index.scenarios: | 182 |
| abstract_inverted_index.selection, | 212 |
| abstract_inverted_index.application | 140 |
| abstract_inverted_index.appropriate | 55 |
| abstract_inverted_index.informative | 121 |
| abstract_inverted_index.methodology | 65 |
| abstract_inverted_index.outperforms | 210 |
| abstract_inverted_index.photometric | 127, 167 |
| abstract_inverted_index.photometry, | 77 |
| abstract_inverted_index.separately. | 110 |
| abstract_inverted_index.very\nlarge | 46 |
| abstract_inverted_index.(Abridged)\n | 236 |
| abstract_inverted_index.Euclid\nDeep | 184 |
| abstract_inverted_index.Survey\nwith | 196 |
| abstract_inverted_index.catastrophic | 158 |
| abstract_inverted_index.facilitating | 31 |
| abstract_inverted_index.improvements | 224 |
| abstract_inverted_index.mock\ngalaxy | 175 |
| abstract_inverted_index.that\nyields | 100 |
| abstract_inverted_index.wavelengths, | 11 |
| abstract_inverted_index.colour-colour | 221 |
| abstract_inverted_index.corresponding | 178 |
| abstract_inverted_index.decision-tree | 91 |
| abstract_inverted_index.deep-learning | 94 |
| abstract_inverted_index.meta-learning | 88 |
| abstract_inverted_index.near-infrared | 15 |
| abstract_inverted_index.of\nquiescent | 68 |
| abstract_inverted_index.significantly | 101 |
| abstract_inverted_index.u-I_E,I_E-J_E | 220 |
| abstract_inverted_index.classification | 172 |
| abstract_inverted_index.classification. | 124 |
| abstract_inverted_index.in\ncombination | 78 |
| abstract_inverted_index.multiwavelength | 80 |
| abstract_inverted_index.semi-supervised | 137 |
| abstract_inverted_index.software.\nHere | 58 |
| abstract_inverted_index.the\nCOSMOS2015 | 166 |
| abstract_inverted_index.Euclid\nI_E-Y_E, | 217 |
| abstract_inverted_index.in\ncompleteness | 225 |
| abstract_inverted_index.machine-learning | 63 |
| abstract_inverted_index.outlier\nfilter, | 143 |
| abstract_inverted_index.ARIADNE\npipeline | 86 |
| abstract_inverted_index.learning\nmethods | 109 |
| abstract_inverted_index.pipeline\nderives | 126 |
| abstract_inverted_index.and\nnear-infrared | 10 |
| abstract_inverted_index.galaxy\nevolution, | 36 |
| abstract_inverted_index.Our\nclassification | 208 |
| abstract_inverted_index.missing\nphotometry | 117 |
| abstract_inverted_index.`sparsity-awareness', | 114 |
| abstract_inverted_index.billion\nastronomical | 29 |
| abstract_inverted_index.spectroscopy,\nacross | 16 |
| abstract_inverted_index.the\n`pseudo-labelling' | 136 |
| abstract_inverted_index.ensembles,\nnearest-neighbours, | 92 |
| cited_by_percentile_year | |
| countries_distinct_count | 5 |
| institutions_distinct_count | 208 |
| citation_normalized_percentile.value | 0.12860831 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |