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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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.

Original language	English
Pages (from-to)	1431-1450
Journal	Monthly Notices of the Royal Astronomical Society
Early online date	30 May 2016
DOIs	https://doi.org/10.1093/mnras/stw1281
Publication status	Published - 11 Sept 2016

Keywords

astro-ph.IM
astro-ph.CO
astro-ph.GA
RCUK
STFC
methods: statistical
techniques: photometric
galaxies: general

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10.1093/mnras/stw1281

stw1281
This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2016 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
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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., ... Costa, L. N. D. (2016). redMaGiC: Selecting Luminous Red Galaxies from the DES Science Verification Data. Monthly Notices of the Royal Astronomical Society, 1431-1450. https://doi.org/10.1093/mnras/stw1281

@article{253959c53f5a42f39d8113a8599dca8d,

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 = "astro-ph.IM, astro-ph.CO, astro-ph.GA, RCUK, STFC, methods: statistical, techniques: photometric, galaxies: general",

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 Costa, {L. N. da}",

year = "2016",

month = sep,

day = "11",

doi = "10.1093/mnras/stw1281",

language = "English",

pages = "1431--1450",

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

issn = "0035-8711",

publisher = "Oxford University Press",

}

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 & Costa, LND 2016, 'redMaGiC: Selecting Luminous Red Galaxies from the DES Science Verification Data', Monthly Notices of the Royal Astronomical Society, 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 - Costa, L. N. da

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 - astro-ph.IM

KW - astro-ph.CO

KW - astro-ph.GA

KW - RCUK

KW - STFC

KW - methods: statistical

KW - techniques: photometric

KW - galaxies: general

U2 - 10.1093/mnras/stw1281

DO - 10.1093/mnras/stw1281

M3 - Article

SN - 0035-8711

SP - 1431

EP - 1450

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

ER -

redMaGiC: Selecting Luminous Red Galaxies from the DES Science Verification Data

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