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Title: redMaGiC: Selecting luminous red galaxies from the DES Science Verification data

Here, 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 ϵ [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.
Authors:
 [1] ;  [2] ;  [1] ;  [3] ;  [4] ;  [5] ;  [6] ;  [7] ;  [7] ;  [2] ;  [8] ;  [7] ;  [9] ;  [4] ;  [7] ;  [10] ;  [11] ;  [7] ;  [12] ;  [2] more »;  [13] ;  [14] ;  [15] ;  [16] ;  [17] ;  [4] ;  [18] ;  [19] ;  [13] ;  [20] ;  [21] ;  [22] ;  [12] ;  [23] ;  [7] ;  [24] ;  [25] ;  [26] ;  [12] ;  [4] ;  [27] ;  [4] ;  [25] ;  [28] ;  [29] ;  [16] ;  [30] ;  [8] ;  [10] ;  [31] ;  [32] ;  [12] ;  [7] ;  [33] ;  [34] ;  [14] ;  [10] ;  [30] ;  [30] ;  [25] ;  [35] ;  [36] ;  [13] ;  [12] ;  [37] ;  [14] ;  [38] ;  [39] ;  [2] ;  [10] ;  [40] ;  [41] ;  [25] ;  [42] ;  [8] ;  [12] ;  [43] ;  [30] ;  [44] ;  [45] ;  [13] ;  [46] ;  [47] ;  [8] ;  [12] ;  [25] ;  [14] « less
  1. Univ. of Arizona, Tucson, AZ (United States)
  2. Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
  3. Univ. Autonoma de Barcelona, Barcelona (Spain)
  4. Institut de Ciencies de l'Espai, Barcelona (Spain)
  5. Stanford Univ., Stanford, CA (United States); ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), Redfern, NSW (Australia); Univ. of Queensland (Australia)
  6. Ludwig-Maximilians Univ. Munchen, Munchen (Germany)
  7. Univ. College London, London (United Kingdom)
  8. National Optical Astronomy Observatory, La Serena (Chile)
  9. Univ. of Cambridge, Cambridge (United Kingdom)
  10. Univ. of Pennsylvania, Philadelphia, PA (United States)
  11. Institut d'Astrophysique de Paris, Paris (France)
  12. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  13. Univ. of Portsmouth, Portsmouth (United Kingdom)
  14. Lab. Interinstitucional de e-Astronomia - LIneA, Rio de Janeiro (Brazil); Observatorio Nacional, Rio de Janeiro (Brazil)
  15. Univ. of Notre Dame, Notre Dame, IN (United States)
  16. Univ. of Illinois, Urbana, IL (United States); National Center for Supercomputing Applications, Urbana, IL (United States)
  17. Univ. Autonoma de Barcelona, Barcelona (Spain); Institut de Ciencies de l'Espai, Barcelona (Spain)
  18. Australian National Univ., Canberra, ACT (Australia)
  19. Stanford Univ., Stanford, CA (United States)
  20. Stanford Univ., Stanford, CA (United States); ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), Redfern, NSW (Australia); Univ. of Queensland, Queensland (Australia)
  21. Texas A & M Univ., College Station, TX (United States)
  22. Excellence Cluster Universe, Garching (Germany); Ludwig-Maximilians Univ., Munich (Germany)
  23. Ludwig-Maximilians Univ. Munchen, Munchen (Germany); Excellence Cluster Universe, Garching (Germany)
  24. Univ. of Pennsylvania, Philadelphia, PA (United States); California Inst. of Technology (CalTech), Pasadena, CA (United States)
  25. Univ. of Michigan, Ann Arbor, MI (United States)
  26. Lab. Interinstitucional de e-Astonomia - LIneA, Rio de Janeiro (Brazil)
  27. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Univ. of Chicago, Chicago, IL (United States)
  28. Swinburne Univ. of Technology (Australia)
  29. Ludwig-Maximilians Univ. Munchen, Munchen (Germany); Max Planck Institute for Extraterrestrial Physics, Garching (Germany)
  30. The Ohio State Univ., Columbus, OH (United States)
  31. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  32. Australian Astronomical Observatory, North Ryde, NSW (Australia)
  33. ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), Redfern, NSW (Australia); Australian Astronomical Observatory, North Ryde, NSW (Australia)
  34. Lab. Interinstitucional de e-Astronomia - LIneA, Rio de Janeiro (Brazil); Univ. de Sao Paulo, Sao Paulo (Brazil)
  35. Univ. Autonoma de Barcelona, Barcelona (Spain); Institucio Catalana de Recerca i Estudis Avancats, Barcelona (Spain)
  36. Excellence Cluster Univ., Garching (Germany); Ludwig-Maximilians Univ., Munich (Germany); Max Planck Institute for Extraterrestrial Physics, Garching (Germany)
  37. Univ. of Queensland, Queensland (Australia)
  38. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  39. Univ. of Sussex, Brighton (United Kingdom)
  40. Centro de Investigaciones Energeticas Medioambientales y Tecnologicas (CIEMAT), Madrid (Spain)
  41. Lab. Interinstitucional de e-Astronomia - LIneA, Rio de Janeiro (Brazil)
  42. Univ. of Illinois, Urbana, IL (United States); Centro de Investigaciones Energeticas, Madrid (Spain)
  43. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Lab. Interinstitucional de e-Astronomia - LIneA, Rio de Janeiro (Brazil)
  44. National Center for Supercomputing Applications, Urbana, IL (United States)
  45. Univ. of Illinois, Urbana, IL (United States)
  46. ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), Redfern, NSW (Australia); Swineburne Univ. of Technology, VIC (Australia)
  47. Argonne National Lab. (ANL), Lemont, IL (United States)
Publication Date:
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Volume: 461; Journal Issue: 2; Journal ID: ISSN 0035-8711
Publisher:
Royal Astronomical Society
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); National Science Foundation (NSF); University of Chicago - Kavli Institute for Cosmological Physics; Ohio State University
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; methods: statistical; techniques: photometric; galaxies: general
OSTI Identifier:
1371551

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-Levy, A., Bernstein, G. M., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Capozzi, D., Rosell, A. Carnero, Carollo, D., Kind, M. Carrasco, Carretero, J., Castander, F. J., Childress, M. J., Cunha, C. E., D'Andrea, C. B., Davis, T., DePoy, D. L., Desai, S., Diehl, H. T., Dietrich, J. P., Doel, P., Eifler, T. F., Evrard, A. E., Neto, A. Fausti, Flaugher, B., Fosalba, P., Frieman, J., Gaztanaga, E., Gerdes, D. W., Glazebrook, K., Gruen, D., Gruendl, R. A., Honscheid, K., James, D. J., Jarvis, M., Kim, A. G., Kuehn, K., Kuropatkin, N., Lahav, O., Lidman, C., Lima, M., Maia, M. A. G., March, M., Martini, P., Melchior, P., Miller, C. J., Miquel, R., Mohr, J. J., Nichol, R. C., Nord, B., O'Neill, C. R., Ogando, R., Plazas, A. A., Romer, A. K., Roodman, A., Sako, M., Sanchez, E., Santiago, B., Schubnell, M., Sevilla-Noarbe, I., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Thaler, J., Thomas, D., Uddin, S., Vikram, V., Walker, A. R., Wester, W., Zhang, Y., and da Costa, L. N.. redMaGiC: Selecting luminous red galaxies from the DES Science Verification data. United States: N. p., Web. doi:10.1093/mnras/stw1281.
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-Levy, A., Bernstein, G. M., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Capozzi, D., Rosell, A. Carnero, Carollo, D., Kind, M. Carrasco, Carretero, J., Castander, F. J., Childress, M. J., Cunha, C. E., D'Andrea, C. B., Davis, T., DePoy, D. L., Desai, S., Diehl, H. T., Dietrich, J. P., Doel, P., Eifler, T. F., Evrard, A. E., Neto, A. Fausti, Flaugher, B., Fosalba, P., Frieman, J., Gaztanaga, E., Gerdes, D. W., Glazebrook, K., Gruen, D., Gruendl, R. A., Honscheid, K., James, D. J., Jarvis, M., Kim, A. G., Kuehn, K., Kuropatkin, N., Lahav, O., Lidman, C., Lima, M., Maia, M. A. G., March, M., Martini, P., Melchior, P., Miller, C. J., Miquel, R., Mohr, J. J., Nichol, R. C., Nord, B., O'Neill, C. R., Ogando, R., Plazas, A. A., Romer, A. K., Roodman, A., Sako, M., Sanchez, E., Santiago, B., Schubnell, M., Sevilla-Noarbe, I., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Thaler, J., Thomas, D., Uddin, S., Vikram, V., Walker, A. R., Wester, W., Zhang, Y., & da Costa, L. N.. redMaGiC: Selecting luminous red galaxies from the DES Science Verification data. United States. doi:10.1093/mnras/stw1281.
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-Levy, A., Bernstein, G. M., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Capozzi, D., Rosell, A. Carnero, Carollo, D., Kind, M. Carrasco, Carretero, J., Castander, F. J., Childress, M. J., Cunha, C. E., D'Andrea, C. B., Davis, T., DePoy, D. L., Desai, S., Diehl, H. T., Dietrich, J. P., Doel, P., Eifler, T. F., Evrard, A. E., Neto, A. Fausti, Flaugher, B., Fosalba, P., Frieman, J., Gaztanaga, E., Gerdes, D. W., Glazebrook, K., Gruen, D., Gruendl, R. A., Honscheid, K., James, D. J., Jarvis, M., Kim, A. G., Kuehn, K., Kuropatkin, N., Lahav, O., Lidman, C., Lima, M., Maia, M. A. G., March, M., Martini, P., Melchior, P., Miller, C. J., Miquel, R., Mohr, J. J., Nichol, R. C., Nord, B., O'Neill, C. R., Ogando, R., Plazas, A. A., Romer, A. K., Roodman, A., Sako, M., Sanchez, E., Santiago, B., Schubnell, M., Sevilla-Noarbe, I., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Thaler, J., Thomas, D., Uddin, S., Vikram, V., Walker, A. R., Wester, W., Zhang, Y., and da Costa, L. N.. 2016. "redMaGiC: Selecting luminous red galaxies from the DES Science Verification data". United States. doi:10.1093/mnras/stw1281. https://www.osti.gov/servlets/purl/1371551.
@article{osti_1371551,
title = {redMaGiC: Selecting luminous red galaxies from the DES Science Verification data},
author = {Rozo, E. and Rykoff, E. S. and Abate, A. and Bonnett, C. and Crocce, M. and Davis, C. and Hoyle, B. and Leistedt, B. and Peiris, H. V. and Wechsler, R. H. and Abbott, T. and Abdalla, F. B. and Banerji, M. and Bauer, A. H. and Benoit-Levy, A. and Bernstein, G. M. and Bertin, E. and Brooks, D. and Buckley-Geer, E. and Burke, D. L. and Capozzi, D. and Rosell, A. Carnero and Carollo, D. and Kind, M. Carrasco and Carretero, J. and Castander, F. J. and Childress, M. J. and Cunha, C. E. and D'Andrea, C. B. and Davis, T. and DePoy, D. L. and Desai, S. and Diehl, H. T. and Dietrich, J. P. and Doel, P. and Eifler, T. F. and Evrard, A. E. and Neto, A. Fausti and Flaugher, B. and Fosalba, P. and Frieman, J. and Gaztanaga, E. and Gerdes, D. W. and Glazebrook, K. and Gruen, D. and Gruendl, R. A. and Honscheid, K. and James, D. J. and Jarvis, M. and Kim, A. G. and Kuehn, K. and Kuropatkin, N. and Lahav, O. and Lidman, C. and Lima, M. and Maia, M. A. G. and March, M. and Martini, P. and Melchior, P. and Miller, C. J. and Miquel, R. and Mohr, J. J. and Nichol, R. C. and Nord, B. and O'Neill, C. R. and Ogando, R. and Plazas, A. A. and Romer, A. K. and Roodman, A. and Sako, M. and Sanchez, E. and Santiago, B. and Schubnell, M. and Sevilla-Noarbe, I. and Smith, R. C. and Soares-Santos, M. and Sobreira, F. and Suchyta, E. and Swanson, M. E. C. and Thaler, J. and Thomas, D. and Uddin, S. and Vikram, V. and Walker, A. R. and Wester, W. and Zhang, Y. and da Costa, L. N.},
abstractNote = {Here, 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 ϵ [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.},
doi = {10.1093/mnras/stw1281},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 461,
place = {United States},
year = {2016},
month = {5}
}