Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

M. Bilicki; H. Hoekstra; M. J. I. Brown; V. Amaro; C. Blake; S. Cavuoti; J. T. A. de Jong; C. Georgiou; H. Hildebrandt; C. Wolf; A. Amon; M. Brescia; S. Brough; M. V. Costa-Duarte; T. Erben; K. Glazebrook; A. Grado; C. Heymans; T. Jarrett; S. Joudaki; K. Kuijken; G. Longo; N. Napolitano; D. Parkinson; C. Vellucci; G. A. Verdoes Kleijn; L. Wang

doi:10.1051/0004-6361/201731942

Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

M. Bilicki
, H. Hoekstra
, M. J. I. Brown
, V. Amaro
, C. Blake
, S. Cavuoti
, J. T. A. de Jong
, C. Georgiou
, H. Hildebrandt
, C. Wolf
, A. Amon
, M. Brescia
, S. Brough
, M. V. Costa-Duarte
, T. Erben
, K. Glazebrook
, A. Grado
, C. Heymans
, T. Jarrett
, S. Joudaki

K. Kuijken, G. Longo, N. Napolitano, D. Parkinson, C. Vellucci, G. A. Verdoes Kleijn, L. Wang

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the BPZ code, at least up to zphot

Original language	English
Article number	A69
Pages (from-to)	1-22
Journal	A&A
Volume	616
DOIs	https://doi.org/10.1051/0004-6361/201731942
Publication status	Published - Aug 2018

Bibliographical note

A&A, in press. Data available from the KiDS website http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php#annz2

Keywords

astro-ph.CO
astro-ph.GA
astro-ph.IM

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10.1051/0004-6361/201731942

aa31942-17Final published version, 2.36 MBLicence: Taverne

Cite this

Bilicki, M., Hoekstra, H., Brown, M. J. I., Amaro, V., Blake, C., Cavuoti, S., Jong, J. T. A. D., Georgiou, C., Hildebrandt, H., Wolf, C., Amon, A., Brescia, M., Brough, S., Costa-Duarte, M. V., Erben, T., Glazebrook, K., Grado, A., Heymans, C., Jarrett, T., ... Wang, L. (2018). Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks. A&A, 616, 1-22. Article A69. https://doi.org/10.1051/0004-6361/201731942

@article{756440dd31b1480ea17b5f497c097fb8,

title = "Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks",

abstract = " We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the BPZ code, at least up to zphot",

keywords = "astro-ph.CO, astro-ph.GA, astro-ph.IM",

author = "M. Bilicki and H. Hoekstra and Brown, \{M. J. I.\} and V. Amaro and C. Blake and S. Cavuoti and Jong, \{J. T. A. de\} and C. Georgiou and H. Hildebrandt and C. Wolf and A. Amon and M. Brescia and S. Brough and Costa-Duarte, \{M. V.\} and T. Erben and K. Glazebrook and A. Grado and C. Heymans and T. Jarrett and S. Joudaki and K. Kuijken and G. Longo and N. Napolitano and D. Parkinson and C. Vellucci and Kleijn, \{G. A. Verdoes\} and L. Wang",

note = "A\&A, in press. Data available from the KiDS website http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php\#annz2",

year = "2018",

month = aug,

doi = "10.1051/0004-6361/201731942",

language = "English",

volume = "616",

pages = "1--22",

journal = "A\&A",

issn = "1406-345X",

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Bilicki, M, Hoekstra, H, Brown, MJI, Amaro, V, Blake, C, Cavuoti, S, Jong, JTAD, Georgiou, C, Hildebrandt, H, Wolf, C, Amon, A, Brescia, M, Brough, S, Costa-Duarte, MV, Erben, T, Glazebrook, K, Grado, A, Heymans, C, Jarrett, T, Joudaki, S, Kuijken, K, Longo, G, Napolitano, N, Parkinson, D, Vellucci, C, Kleijn, GAV & Wang, L 2018, 'Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks', A&A, vol. 616, A69, pp. 1-22. https://doi.org/10.1051/0004-6361/201731942

TY - JOUR

T1 - Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

AU - Bilicki, M.

AU - Hoekstra, H.

AU - Brown, M. J. I.

AU - Amaro, V.

AU - Blake, C.

AU - Cavuoti, S.

AU - Jong, J. T. A. de

AU - Georgiou, C.

AU - Hildebrandt, H.

AU - Wolf, C.

AU - Amon, A.

AU - Brescia, M.

AU - Brough, S.

AU - Costa-Duarte, M. V.

AU - Erben, T.

AU - Glazebrook, K.

AU - Grado, A.

AU - Heymans, C.

AU - Jarrett, T.

AU - Joudaki, S.

AU - Kuijken, K.

AU - Longo, G.

AU - Napolitano, N.

AU - Parkinson, D.

AU - Vellucci, C.

AU - Kleijn, G. A. Verdoes

AU - Wang, L.

N1 - A&A, in press. Data available from the KiDS website http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php#annz2

PY - 2018/8

Y1 - 2018/8

N2 - We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the BPZ code, at least up to zphot

AB - We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the BPZ code, at least up to zphot

KW - astro-ph.CO

KW - astro-ph.GA

KW - astro-ph.IM

U2 - 10.1051/0004-6361/201731942

DO - 10.1051/0004-6361/201731942

M3 - Article

SN - 1406-345X

VL - 616

SP - 1

EP - 22

JO - A&A

JF - A&A

M1 - A69

ER -

Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

Abstract

Bibliographical note

Keywords

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