RedMaGiC: Selecting luminous red galaxies from the DES Science Verification data

E. Rozo; E. S. Rykoff; A. Abate; C. Bonnett; M. Crocce; C. Davis; B. Hoyle; B. Leistedt; H. V. Peiris; R. H. Wechsler; T. Abbott; F. B. Abdalla; M. Banerji; A. H. Bauer; A. Benoit-Lévy; G. M. Bernstein; E. Bertin; D. Brooks; E. Buckley-Geer; D. L. Burke; D. Capozzi; A. Carnero Rosell; D. Carollo; M. Carrasco Kind; J. Carretero; F. J. Castander; M. J. Childress; C. E. Cunha; C. B. D'Andrea; T. Davis; D. L. DePoy; S. Desai; H. T. Diehl; J. P. Dietrich; P. Doel; T. F. Eifler; A. E. Evrard; A. Fausti Neto; B. Flaugher; P. Fosalba; J. Frieman; E. Gaztanaga; D. W. Gerdes; K. Glazebrook; D. Gruen; R. A. Gruendl; K. Honscheid; D. J. James; M. Jarvis; A. G. Kim; K. Kuehn; N. Kuropatkin; O. Lahav; C. Lidman; M. Lima; M. A.G. Maia; M. March; P. Martini; P. Melchior; C. J. Miller; R. Miquel; J. J. Mohr; R. C. Nichol; B. Nord; C. R. O'Neill; R. Ogando; A. A. Plazas; A. K. Romer; A. Roodman; M. Sako; E. Sanchez; B. Santiago; M. Schubnell; I. Sevilla-Noarbe; R. C. Smith; M. Soares-Santos; F. Sobreira; E. Suchyta; M. E.C. Swanson; J. Thaler; D. Thomas; S. Uddin; V. Vikram; A. R. Walker; W. Wester; Y. Zhang; L. N. da Costa

doi:10.1093/mnras/stw1281

RedMaGiC: Selecting luminous red galaxies from the DES Science Verification data

E. Rozo, E. S. Rykoff, A. Abate, C. Bonnett, M. Crocce, C. Davis, B. Hoyle, B. Leistedt, H. V. Peiris, R. H. Wechsler, T. Abbott, F. B. Abdalla, M. Banerji, A. H. Bauer, A. Benoit-Lévy, G. M. Bernstein, E. Bertin, D. Brooks, E. Buckley-Geer, D. L. BurkeD. Capozzi, A. Carnero Rosell, D. Carollo, M. Carrasco Kind, J. Carretero, F. J. Castander, M. J. Childress, C. E. Cunha, C. B. D'Andrea, T. Davis, D. L. DePoy, S. Desai, H. T. Diehl, J. P. Dietrich, P. Doel, T. F. Eifler, A. E. Evrard, A. Fausti Neto, B. Flaugher, P. Fosalba, J. Frieman, E. Gaztanaga, D. W. Gerdes, K. Glazebrook, D. Gruen, R. A. Gruendl, K. Honscheid, D. J. James, M. Jarvis, A. G. Kim, K. Kuehn, N. Kuropatkin, O. Lahav, C. Lidman, M. Lima, M. A.G. Maia, M. March, P. Martini, P. Melchior, C. J. Miller, R. Miquel, J. J. Mohr, R. C. Nichol, B. Nord, C. R. O'Neill, R. Ogando, A. A. Plazas, A. K. Romer, A. Roodman, M. Sako, E. Sanchez, B. Santiago, M. Schubnell, I. Sevilla-Noarbe, R. C. Smith, M. Soares-Santos, F. Sobreira, E. Suchyta, M. E.C. Swanson, J. Thaler, D. Thomas, S. Uddin, V. Vikram, A. R. Walker, W. Wester, Y. Zhang, L. N. da Costa

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163 Scopus citations

Abstract

We introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z ∈ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10^-3 (h^-1 Mpc)^-3, and a median photo-z bias (z_spec - z_photo) and scatter (σ_z/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.

Original language	English
Pages (from-to)	1431-1450
Number of pages	20
Journal	Monthly Notices of the Royal Astronomical Society
Volume	461
Issue number	2
DOIs	https://doi.org/10.1093/mnras/stw1281
State	Published - Sep 11 2016
Externally published	Yes

Funding

Funders	Funder number
Seventh Framework Programme	1138766, 240672, 306478, 291329

Keywords

Galaxies: general
Methods: statistical
Techniques: photometric

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10.1093/mnras/stw1281

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Rozo, E., Rykoff, E. S., Abate, A., Bonnett, C., Crocce, M., Davis, C., Hoyle, B., Leistedt, B., Peiris, H. V., Wechsler, R. H., Abbott, T., Abdalla, F. B., Banerji, M., Bauer, A. H., Benoit-Lévy, A., Bernstein, G. M., Bertin, E., Brooks, D., Buckley-Geer, E., ... da Costa, L. N. (2016). RedMaGiC: Selecting luminous red galaxies from the DES Science Verification data. Monthly Notices of the Royal Astronomical Society, 461(2), 1431-1450. https://doi.org/10.1093/mnras/stw1281

@article{c9ff7bdf1eae4406bfd7109163bd5629,

title = "RedMaGiC: Selecting luminous red galaxies from the DES Science Verification data",

abstract = "We introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z ∈ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10-3 (h-1 Mpc)-3, and a median photo-z bias (zspec - zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.",

keywords = "Galaxies: general, Methods: statistical, Techniques: photometric",

author = "E. Rozo and Rykoff, {E. S.} and A. Abate and C. Bonnett and M. Crocce and C. Davis and B. Hoyle and B. Leistedt and Peiris, {H. V.} and Wechsler, {R. H.} and T. Abbott and Abdalla, {F. B.} and M. Banerji and Bauer, {A. H.} and A. Benoit-L{\'e}vy and Bernstein, {G. M.} and E. Bertin and D. Brooks and E. Buckley-Geer and Burke, {D. L.} and D. Capozzi and Rosell, {A. Carnero} and D. Carollo and Kind, {M. Carrasco} and J. Carretero and Castander, {F. J.} and Childress, {M. J.} and Cunha, {C. E.} and D'Andrea, {C. B.} and T. Davis and DePoy, {D. L.} and S. Desai and Diehl, {H. T.} and Dietrich, {J. P.} and P. Doel and Eifler, {T. F.} and Evrard, {A. E.} and Neto, {A. Fausti} and B. Flaugher and P. Fosalba and J. Frieman and E. Gaztanaga and Gerdes, {D. W.} and K. Glazebrook and D. Gruen and Gruendl, {R. A.} and K. Honscheid and James, {D. J.} and M. Jarvis and Kim, {A. G.} and K. Kuehn and N. Kuropatkin and O. Lahav and C. Lidman and M. Lima and Maia, {M. A.G.} and M. March and P. Martini and P. Melchior and Miller, {C. J.} and R. Miquel and Mohr, {J. J.} and Nichol, {R. C.} and B. Nord and O'Neill, {C. R.} and R. Ogando and Plazas, {A. A.} and Romer, {A. K.} and A. Roodman and M. Sako and E. Sanchez and B. Santiago and M. Schubnell and I. Sevilla-Noarbe and Smith, {R. C.} and M. Soares-Santos and F. Sobreira and E. Suchyta and Swanson, {M. E.C.} and J. Thaler and D. Thomas and S. Uddin and V. Vikram and Walker, {A. R.} and W. Wester and Y. Zhang and {da Costa}, {L. N.}",

note = "Publisher Copyright: {\textcopyright} 2016 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.",

year = "2016",

month = sep,

day = "11",

doi = "10.1093/mnras/stw1281",

language = "English",

volume = "461",

pages = "1431--1450",

journal = "Monthly Notices of the Royal Astronomical Society",

issn = "0035-8711",

publisher = "Oxford University Press",

number = "2",

}

Rozo, E, Rykoff, ES, Abate, A, Bonnett, C, Crocce, M, Davis, C, Hoyle, B, Leistedt, B, Peiris, HV, Wechsler, RH, Abbott, T, Abdalla, FB, Banerji, M, Bauer, AH, Benoit-Lévy, A, Bernstein, GM, Bertin, E, Brooks, D, Buckley-Geer, E, Burke, DL, Capozzi, D, Rosell, AC, Carollo, D, Kind, MC, Carretero, J, Castander, FJ, Childress, MJ, Cunha, CE, D'Andrea, CB, Davis, T, DePoy, DL, Desai, S, Diehl, HT, Dietrich, JP, Doel, P, Eifler, TF, Evrard, AE, Neto, AF, Flaugher, B, Fosalba, P, Frieman, J, Gaztanaga, E, Gerdes, DW, Glazebrook, K, Gruen, D, Gruendl, RA, Honscheid, K, James, DJ, Jarvis, M, Kim, AG, Kuehn, K, Kuropatkin, N, Lahav, O, Lidman, C, Lima, M, Maia, MAG, March, M, Martini, P, Melchior, P, Miller, CJ, Miquel, R, Mohr, JJ, Nichol, RC, Nord, B, O'Neill, CR, Ogando, R, Plazas, AA, Romer, AK, Roodman, A, Sako, M, Sanchez, E, Santiago, B, Schubnell, M, Sevilla-Noarbe, I, Smith, RC, Soares-Santos, M, Sobreira, F, Suchyta, E, Swanson, MEC, Thaler, J, Thomas, D, Uddin, S, Vikram, V, Walker, AR, Wester, W, Zhang, Y & da Costa, LN 2016, 'RedMaGiC: Selecting luminous red galaxies from the DES Science Verification data', Monthly Notices of the Royal Astronomical Society, vol. 461, no. 2, pp. 1431-1450. https://doi.org/10.1093/mnras/stw1281

TY - JOUR

T1 - RedMaGiC

T2 - Selecting luminous red galaxies from the DES Science Verification data

AU - Rozo, E.

AU - Rykoff, E. S.

AU - Abate, A.

AU - Bonnett, C.

AU - Crocce, M.

AU - Davis, C.

AU - Hoyle, B.

AU - Leistedt, B.

AU - Peiris, H. V.

AU - Wechsler, R. H.

AU - Abbott, T.

AU - Abdalla, F. B.

AU - Banerji, M.

AU - Bauer, A. H.

AU - Benoit-Lévy, A.

AU - Bernstein, G. M.

AU - Bertin, E.

AU - Brooks, D.

AU - Buckley-Geer, E.

AU - Burke, D. L.

AU - Capozzi, D.

AU - Rosell, A. Carnero

AU - Carollo, D.

AU - Kind, M. Carrasco

AU - Carretero, J.

AU - Castander, F. J.

AU - Childress, M. J.

AU - Cunha, C. E.

AU - D'Andrea, C. B.

AU - Davis, T.

AU - DePoy, D. L.

AU - Desai, S.

AU - Diehl, H. T.

AU - Dietrich, J. P.

AU - Doel, P.

AU - Eifler, T. F.

AU - Evrard, A. E.

AU - Neto, A. Fausti

AU - Flaugher, B.

AU - Fosalba, P.

AU - Frieman, J.

AU - Gaztanaga, E.

AU - Gerdes, D. W.

AU - Glazebrook, K.

AU - Gruen, D.

AU - Gruendl, R. A.

AU - Honscheid, K.

AU - James, D. J.

AU - Jarvis, M.

AU - Kim, A. G.

AU - Kuehn, K.

AU - Kuropatkin, N.

AU - Lahav, O.

AU - Lidman, C.

AU - Lima, M.

AU - Maia, M. A.G.

AU - March, M.

AU - Martini, P.

AU - Melchior, P.

AU - Miller, C. J.

AU - Miquel, R.

AU - Mohr, J. J.

AU - Nichol, R. C.

AU - Nord, B.

AU - O'Neill, C. R.

AU - Ogando, R.

AU - Plazas, A. A.

AU - Romer, A. K.

AU - Roodman, A.

AU - Sako, M.

AU - Sanchez, E.

AU - Santiago, B.

AU - Schubnell, M.

AU - Sevilla-Noarbe, I.

AU - Smith, R. C.

AU - Soares-Santos, M.

AU - Sobreira, F.

AU - Suchyta, E.

AU - Swanson, M. E.C.

AU - Thaler, J.

AU - Thomas, D.

AU - Uddin, S.

AU - Vikram, V.

AU - Walker, A. R.

AU - Wester, W.

AU - Zhang, Y.

AU - da Costa, L. N.

PY - 2016/9/11

Y1 - 2016/9/11

N2 - We introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z ∈ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10-3 (h-1 Mpc)-3, and a median photo-z bias (zspec - zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.

AB - We introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z ∈ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10-3 (h-1 Mpc)-3, and a median photo-z bias (zspec - zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.

KW - Galaxies: general

KW - Methods: statistical

KW - Techniques: photometric

UR - http://www.scopus.com/inward/record.url?scp=84982267595&partnerID=8YFLogxK

U2 - 10.1093/mnras/stw1281

DO - 10.1093/mnras/stw1281

M3 - Article

AN - SCOPUS:84982267595

SN - 0035-8711

VL - 461

SP - 1431

EP - 1450

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

IS - 2

ER -

RedMaGiC: Selecting luminous red galaxies from the DES Science Verification data

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