Dark Energy Survey Year 1 results: Cross-correlation redshifts - methods and systematics characterization

M. Gatti; P. Vielzeuf; C. Davis; R. Cawthon; M. M. Rau; J. DeRose; J. De Vicente; A. Alarcon; E. Rozo; E. Gaztanaga; B. Hoyle; R. Miquel; G. M. Bernstein; C. Bonnett; A. Carnero Rosell; F. J. Castander; C. Chang; L. N. da Costa; D. Gruen; J. Gschwend; W. G. Hartley; H. Lin; N. MacCrann; M. A.G. Maia; R. L.C. Ogando; A. Roodman; I. Sevilla-Noarbe; M. A. Troxel; R. H. Wechsler; J. Asorey; T. M. Davis; K. Glazebrook; S. R. Hinton; G. Lewis; C. Lidman; E. Macaulay; A. Möller; C. R. O'Neill; N. E. Sommer; S. A. Uddin; F. Yuan; B. Zhang; T. M.C. Abbott; S. Allam; J. Annis; K. Bechtol; D. Brooks; D. L. Burke; D. Carollo; M. Carrasco Kind; J. Carretero; C. E. Cunha; C. B. D'Andrea; D. L. DePoy; S. Desai; T. F. Eifler; A. E. Evrard; B. Flaugher; P. Fosalba; J. Frieman; J. García-Bellido; D. W. Gerdes; D. A. Goldstein; R. A. Gruendl; G. Gutierrez; K. Honscheid; J. K. Hoormann; B. Jain; D. J. James; M. Jarvis; T. Jeltema; M. W.G. Johnson; M. D. Johnson; E. Krause; K. Kuehn; S. Kuhlmann; N. Kuropatkin; T. S. Li; M. Lima; J. L. Marshall; P. Melchior; F. Menanteau; R. C. Nichol; B. Nord; A. A. Plazas; K. Reil; E. S. Rykoff; M. Sako; E. Sanchez; V. Scarpine; M. Schubnell; E. Sheldon; M. Smith; R. C. Smith; M. Soares-Santos; F. Sobreira; E. Suchyta; M. E.C. Swanson; G. Tarle; D. Thomas

doi:10.1093/mnras/sty466

Dark Energy Survey Year 1 results: Cross-correlation redshifts - methods and systematics characterization

M. Gatti, P. Vielzeuf, C. Davis, R. Cawthon, M. M. Rau, J. DeRose, J. De Vicente, A. Alarcon, E. Rozo, E. Gaztanaga, B. Hoyle, R. Miquel, G. M. Bernstein, C. Bonnett, A. Carnero Rosell, F. J. Castander, C. Chang, L. N. da Costa, D. Gruen, J. GschwendW. G. Hartley, H. Lin, N. MacCrann, M. A.G. Maia, R. L.C. Ogando, A. Roodman, I. Sevilla-Noarbe, M. A. Troxel, R. H. Wechsler, J. Asorey, T. M. Davis, K. Glazebrook, S. R. Hinton, G. Lewis, C. Lidman, E. Macaulay, A. Möller, C. R. O'Neill, N. E. Sommer, S. A. Uddin, F. Yuan, B. Zhang, T. M.C. Abbott, S. Allam, J. Annis, K. Bechtol, D. Brooks, D. L. Burke, D. Carollo, M. Carrasco Kind, J. Carretero, C. E. Cunha, C. B. D'Andrea, D. L. DePoy, S. Desai, T. F. Eifler, A. E. Evrard, B. Flaugher, P. Fosalba, J. Frieman, J. García-Bellido, D. W. Gerdes, D. A. Goldstein, R. A. Gruendl, G. Gutierrez, K. Honscheid, J. K. Hoormann, B. Jain, D. J. James, M. Jarvis, T. Jeltema, M. W.G. Johnson, M. D. Johnson, E. Krause, K. Kuehn, S. Kuhlmann, N. Kuropatkin, T. S. Li, M. Lima, J. L. Marshall, P. Melchior, F. Menanteau, R. C. Nichol, B. Nord, A. A. Plazas, K. Reil, E. S. Rykoff, M. Sako, E. Sanchez, V. Scarpine, M. Schubnell, E. Sheldon, M. Smith, R. C. Smith, M. Soares-Santos, F. Sobreira, E. Suchyta, M. E.C. Swanson, G. Tarle, D. Thomas

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© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing source galaxies from the Dark Energy Survey Year 1 sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample.We apply the method to two photo-z codes run in our simulated data: Bayesian Photometric Redshift and Directional Neighbourhood Fitting. We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering versus photo-zs. The systematic uncertainty in the mean redshift bias of the source galaxy sample is Δz ≲ 0.02, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.

Idioma original	Anglès
Pàgines (de-a)	1664-1682
Revista	Monthly Notices of the Royal Astronomical Society
Volum	477
Número	2
DOIs	https://doi.org/10.1093/mnras/sty466
Estat de la publicació	Publicada - 21 de juny 2018

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10.1093/mnras/sty466

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Gatti, M., Vielzeuf, P., Davis, C., Cawthon, R., Rau, M. M., DeRose, J., De Vicente, J., Alarcon, A., Rozo, E., Gaztanaga, E., Hoyle, B., Miquel, R., Bernstein, G. M., Bonnett, C., Rosell, A. C., Castander, F. J., Chang, C., da Costa, L. N., Gruen, D., ... Thomas, D. (2018). Dark Energy Survey Year 1 results: Cross-correlation redshifts - methods and systematics characterization. Monthly Notices of the Royal Astronomical Society, 477(2), 1664-1682. https://doi.org/10.1093/mnras/sty466

@article{1897faf2d37b426c94917db3058ce415,

title = "Dark Energy Survey Year 1 results: Cross-correlation redshifts - methods and systematics characterization",

abstract = "{\textcopyright} 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing source galaxies from the Dark Energy Survey Year 1 sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample.We apply the method to two photo-z codes run in our simulated data: Bayesian Photometric Redshift and Directional Neighbourhood Fitting. We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering versus photo-zs. The systematic uncertainty in the mean redshift bias of the source galaxy sample is Δz ≲ 0.02, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.",

keywords = "Cosmology: Observations, Galaxies: Distances and redshifts",

author = "M. Gatti and P. Vielzeuf and C. Davis and R. Cawthon and Rau, {M. M.} and J. DeRose and {De Vicente}, J. and A. Alarcon and E. Rozo and E. Gaztanaga and B. Hoyle and R. Miquel and Bernstein, {G. M.} and C. Bonnett and Rosell, {A. Carnero} and Castander, {F. J.} and C. Chang and {da Costa}, {L. N.} and D. Gruen and J. Gschwend and Hartley, {W. G.} and H. Lin and N. MacCrann and Maia, {M. A.G.} and Ogando, {R. L.C.} and A. Roodman and I. Sevilla-Noarbe and Troxel, {M. A.} and Wechsler, {R. H.} and J. Asorey and Davis, {T. M.} and K. Glazebrook and Hinton, {S. R.} and G. Lewis and C. Lidman and E. Macaulay and A. M{\"o}ller and O'Neill, {C. R.} and Sommer, {N. E.} and Uddin, {S. A.} and F. Yuan and B. Zhang and Abbott, {T. M.C.} and S. Allam and J. Annis and K. Bechtol and D. Brooks and Burke, {D. L.} and D. Carollo and Kind, {M. Carrasco} and J. Carretero and Cunha, {C. E.} and D'Andrea, {C. B.} and DePoy, {D. L.} and S. Desai and Eifler, {T. F.} and Evrard, {A. E.} and B. Flaugher and P. Fosalba and J. Frieman and J. Garc{\'i}a-Bellido and Gerdes, {D. W.} and Goldstein, {D. A.} and Gruendl, {R. A.} and G. Gutierrez and K. Honscheid and Hoormann, {J. K.} and B. Jain and James, {D. J.} and M. Jarvis and T. Jeltema and Johnson, {M. W.G.} and Johnson, {M. D.} and E. Krause and K. Kuehn and S. Kuhlmann and N. Kuropatkin and Li, {T. S.} and M. Lima and Marshall, {J. L.} and P. Melchior and F. Menanteau and Nichol, {R. C.} and B. Nord and Plazas, {A. A.} and K. Reil and Rykoff, {E. S.} and M. Sako and E. Sanchez and V. Scarpine and M. Schubnell and E. Sheldon and M. Smith and Smith, {R. C.} and M. Soares-Santos and F. Sobreira and E. Suchyta and Swanson, {M. E.C.} and G. Tarle and D. Thomas",

year = "2018",

month = jun,

day = "21",

doi = "10.1093/mnras/sty466",

language = "English",

volume = "477",

pages = "1664--1682",

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

issn = "0035-8711",

number = "2",

}

Gatti, M, Vielzeuf, P, Davis, C, Cawthon, R, Rau, MM, DeRose, J, De Vicente, J, Alarcon, A, Rozo, E, Gaztanaga, E, Hoyle, B, Miquel, R, Bernstein, GM, Bonnett, C, Rosell, AC, Castander, FJ, Chang, C, da Costa, LN, Gruen, D, Gschwend, J, Hartley, WG, Lin, H, MacCrann, N, Maia, MAG, Ogando, RLC, Roodman, A, Sevilla-Noarbe, I, Troxel, MA, Wechsler, RH, Asorey, J, Davis, TM, Glazebrook, K, Hinton, SR, Lewis, G, Lidman, C, Macaulay, E, Möller, A, O'Neill, CR, Sommer, NE, Uddin, SA, Yuan, F, Zhang, B, Abbott, TMC, Allam, S, Annis, J, Bechtol, K, Brooks, D, Burke, DL, Carollo, D, Kind, MC, Carretero, J, Cunha, CE, D'Andrea, CB, DePoy, DL, Desai, S, Eifler, TF, Evrard, AE, Flaugher, B, Fosalba, P, Frieman, J, García-Bellido, J, Gerdes, DW, Goldstein, DA, Gruendl, RA, Gutierrez, G, Honscheid, K, Hoormann, JK, Jain, B, James, DJ, Jarvis, M, Jeltema, T, Johnson, MWG, Johnson, MD, Krause, E, Kuehn, K, Kuhlmann, S, Kuropatkin, N, Li, TS, Lima, M, Marshall, JL, Melchior, P, Menanteau, F, Nichol, RC, Nord, B, Plazas, AA, Reil, K, Rykoff, ES, Sako, M, Sanchez, E, Scarpine, V, Schubnell, M, Sheldon, E, Smith, M, Smith, RC, Soares-Santos, M, Sobreira, F, Suchyta, E, Swanson, MEC, Tarle, G & Thomas, D 2018, 'Dark Energy Survey Year 1 results: Cross-correlation redshifts - methods and systematics characterization', Monthly Notices of the Royal Astronomical Society, vol. 477, núm. 2, pàg. 1664-1682. https://doi.org/10.1093/mnras/sty466

TY - JOUR

T1 - Dark Energy Survey Year 1 results: Cross-correlation redshifts - methods and systematics characterization

AU - Gatti, M.

AU - Vielzeuf, P.

AU - Davis, C.

AU - Cawthon, R.

AU - Rau, M. M.

AU - DeRose, J.

AU - De Vicente, J.

AU - Alarcon, A.

AU - Rozo, E.

AU - Gaztanaga, E.

AU - Hoyle, B.

AU - Miquel, R.

AU - Bernstein, G. M.

AU - Bonnett, C.

AU - Rosell, A. Carnero

AU - Castander, F. J.

AU - Chang, C.

AU - da Costa, L. N.

AU - Gruen, D.

AU - Gschwend, J.

AU - Hartley, W. G.

AU - Lin, H.

AU - MacCrann, N.

AU - Maia, M. A.G.

AU - Ogando, R. L.C.

AU - Roodman, A.

AU - Sevilla-Noarbe, I.

AU - Troxel, M. A.

AU - Wechsler, R. H.

AU - Asorey, J.

AU - Davis, T. M.

AU - Glazebrook, K.

AU - Hinton, S. R.

AU - Lewis, G.

AU - Lidman, C.

AU - Macaulay, E.

AU - Möller, A.

AU - O'Neill, C. R.

AU - Sommer, N. E.

AU - Uddin, S. A.

AU - Yuan, F.

AU - Zhang, B.

AU - Abbott, T. M.C.

AU - Allam, S.

AU - Annis, J.

AU - Bechtol, K.

AU - Brooks, D.

AU - Burke, D. L.

AU - Carollo, D.

AU - Kind, M. Carrasco

AU - Carretero, J.

AU - Cunha, C. E.

AU - D'Andrea, C. B.

AU - DePoy, D. L.

AU - Desai, S.

AU - Eifler, T. F.

AU - Evrard, A. E.

AU - Flaugher, B.

AU - Fosalba, P.

AU - Frieman, J.

AU - García-Bellido, J.

AU - Gerdes, D. W.

AU - Goldstein, D. A.

AU - Gruendl, R. A.

AU - Gutierrez, G.

AU - Honscheid, K.

AU - Hoormann, J. K.

AU - Jain, B.

AU - James, D. J.

AU - Jarvis, M.

AU - Jeltema, T.

AU - Johnson, M. W.G.

AU - Johnson, M. D.

AU - Krause, E.

AU - Kuehn, K.

AU - Kuhlmann, S.

AU - Kuropatkin, N.

AU - Li, T. S.

AU - Lima, M.

AU - Marshall, J. L.

AU - Melchior, P.

AU - Menanteau, F.

AU - Nichol, R. C.

AU - Nord, B.

AU - Plazas, A. A.

AU - Reil, K.

AU - Rykoff, E. S.

AU - Sako, M.

AU - Sanchez, E.

AU - Scarpine, V.

AU - Schubnell, M.

AU - Sheldon, E.

AU - Smith, M.

AU - Smith, R. C.

AU - Soares-Santos, M.

AU - Sobreira, F.

AU - Suchyta, E.

AU - Swanson, M. E.C.

AU - Tarle, G.

AU - Thomas, D.

PY - 2018/6/21

Y1 - 2018/6/21

N2 - © 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing source galaxies from the Dark Energy Survey Year 1 sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample.We apply the method to two photo-z codes run in our simulated data: Bayesian Photometric Redshift and Directional Neighbourhood Fitting. We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering versus photo-zs. The systematic uncertainty in the mean redshift bias of the source galaxy sample is Δz ≲ 0.02, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.

AB - © 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing source galaxies from the Dark Energy Survey Year 1 sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample.We apply the method to two photo-z codes run in our simulated data: Bayesian Photometric Redshift and Directional Neighbourhood Fitting. We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering versus photo-zs. The systematic uncertainty in the mean redshift bias of the source galaxy sample is Δz ≲ 0.02, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.

KW - Cosmology: Observations

KW - Galaxies: Distances and redshifts

U2 - 10.1093/mnras/sty466

DO - 10.1093/mnras/sty466

M3 - Article

SN - 0035-8711

VL - 477

SP - 1664

EP - 1682

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

IS - 2

ER -

Dark Energy Survey Year 1 results: Cross-correlation redshifts - methods and systematics characterization

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