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Title: Dark Energy Survey Year 1 results: cross-correlation redshifts – methods and systematics characterization

Abstract

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.

Authors:
 [1];  [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [7]; ORCiD logo [4];  [9];  [10];  [1];  [11];  [7];  [3];  [11];  [12];  [11] more »;  [13];  [14];  [15];  [11];  [11];  [12];  [6];  [15];  [16];  [17];  [17];  [18];  [19];  [20];  [21];  [19];  [22];  [23];  [22];  [24];  [22];  [22];  [25];  [14];  [14];  [26];  [27];  [12];  [28];  [29];  [1];  [2];  [10];  [30];  [31];  [32];  [33];  [14];  [7];  [34];  [35];  [33];  [36];  [29];  [14];  [15];  [19];  [10];  [37];  [10];  [38];  [39];  [39];  [2];  [40];  [41];  [14];  [14];  [42];  [30];  [43];  [44];  [45];  [14];  [46];  [47];  [12];  [10];  [6];  [14];  [48];  [49];  [50];  [25];  [14];  [51];  [52];  [53];  [48];  [45];  [22];  [14];  [41];  [25];  [54];  [14];  [10] « less
+ Show Author Affiliations
  1. Institut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra (Barcelona), Spain
  2. Kavli Institute for Particle Astrophysics and Cosmology, PO Box 2450, Stanford University, Stanford, CA 94305, USA
  3. Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
  4. Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians Universität München, Scheinerstr. 1, D-81679 München, Germany
  5. Kavli Institute for Particle Astrophysics and Cosmology, PO Box 2450, Stanford University, Stanford, CA 94305, USA; Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA
  6. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), E-28040 Madrid, Spain
  7. Institute of Space Sciences, IEEC-CSIC, Campus UAB, Carrer de Can Magrans, s/n, E-08193 Barcelona, Spain
  8. Department of Physics, University of Arizona, Tucson, AZ 85721, USA
  9. Institut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra (Barcelona), Spain; Institució Catalana de Recerca i Estudis Avançats, E-08010 Barcelona, Spain
  10. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
  11. Laboratório Interinstitucional de e-Astronomia -- LIneA, Rua Gal. José Cristino 77, 20921-400 Rio de Janeiro, RJ, Brazil; Observatório Nacional, Rua Gal. José Cristino 77, 20921-400 Rio de Janeiro, RJ, Brazil
  12. Kavli Institute for Particle Astrophysics and Cosmology, PO Box 2450, Stanford University, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  13. Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK; Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 16, CH-8093 Zurich, Switzerland
  14. Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA
  15. Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA; Department of Physics, The Ohio State University, Columbus, OH 43210, USA
  16. Kavli Institute for Particle Astrophysics and Cosmology, PO Box 2450, Stanford University, Stanford, CA 94305, USA; Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  17. ARC Centre of Excellence for All-sky Astrophysics (CAASTRO); School of Mathematics and Physics, University of Queensland, Brisbane, QLD 4072, Australia
  18. Centre for Astrophysics and Supercomputing, Swinburne University of Technology, Victoria 3122, Australia
  19. School of Mathematics and Physics, University of Queensland, Brisbane, QLD 4072, Australia
  20. ARC Centre of Excellence for All-sky Astrophysics (CAASTRO); Sydney Institute for Astronomy, School of Physics A28, The University of Sydney, NSW 2006, Australia
  21. ARC Centre of Excellence for All-sky Astrophysics (CAASTRO); Australian Astronomical Observatory, North Ryde, NSW 2113, Australia
  22. ARC Centre of Excellence for All-sky Astrophysics (CAASTRO); The Research School of Astronomy and Astrophysics, Australian National University, ACT 2601, Australia
  23. ARC Centre of Excellence for All-sky Astrophysics (CAASTRO)
  24. ARC Centre of Excellence for All-sky Astrophysics (CAASTRO); Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing, Jiangshu 210008, China
  25. Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, Casilla 603, La Serena, Chile
  26. LSST, 933 North Cherry Avenue, Tucson, AZ 85721, USA
  27. Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK
  28. ARC Centre of Excellence for All-sky Astrophysics (CAASTRO); INAF -- Osservatorio Astrofisico di Torino, via Osservatorio 20, I-10025 Pino Torinese, Italy
  29. Department of Astronomy, University of Illinois, 1002 W. Green Street, Urbana, IL 61801, USA; National Center for Supercomputing Applications, 1205 West Clark St, Urbana, IL 61801, USA
  30. George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, and Department of Physics and Astronomy, Texas A&M University, College Station, TX 77843, USA
  31. Department of Physics, IIT Hyderabad, Kandi, Telangana 502285, India
  32. Department of Physics, California Institute of Technology, Pasadena, CA 91125, USA; Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109, USA
  33. Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA; Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
  34. Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA; Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA
  35. Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, E-28049 Madrid, Spain
  36. Department of Astronomy, University of California, Berkeley, 501 Campbell Hall, Berkeley, CA 94720, USA; Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
  37. Astronomy Department, University of Washington, Box 351580, Seattle, WA 98195, USA
  38. Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA
  39. INAF -- Osservatorio Astrofisico di Torino, via Osservatorio 20, I-10025 Pino Torinese, Italy
  40. Australian Astronomical Observatory, North Ryde, NSW 2113, Australia
  41. Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
  42. Laboratório Interinstitucional de e-Astronomia -- LIneA, Rua Gal. José Cristino 77, 20921-400 Rio de Janeiro, RJ, Brazil; Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, CP 66318, 05314-970 São Paulo, SP, Brazil
  43. Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA
  44. INAF -- Osservatorio Astrofisico di Torino, via Osservatorio 20, I-10025 Pino Torinese, Italy; Department of Astronomy, University of Illinois, 1002 W. Green Street, Urbana, IL 61801, USA
  45. Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX, UK
  46. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109, USA
  47. SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  48. Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
  49. Brookhaven National Laboratory, Bldg 510, Upton, NY 11973, USA
  50. School of Physics and Astronomy, University of Southampton, Southampton SO17 1BJ, UK
  51. Laboratório Interinstitucional de e-Astronomia -- LIneA, Rua Gal. José Cristino 77, 20921-400 Rio de Janeiro, RJ, Brazil; Instituto de Física Gleb Wataghin, Universidade Estadual de Campinas, 13083-859 Campinas, SP, Brazil
  52. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
  53. National Center for Supercomputing Applications, 1205 West Clark St, Urbana, IL 61801, USA
  54. Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians Universität München, Scheinerstr. 1, D-81679 München, Germany; Excellence Cluster Universe, Boltzmannstr. 2, D-85748 Garching, Germany; Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, D-85748 Garching, Germany
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Brookhaven National Lab. (BNL), Upton, NY (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Contributing Org.:
DES Collaboration
OSTI Identifier:
1439283
Alternate Identifier(s):
OSTI ID: 1399673; OSTI ID: 1468040; OSTI ID: 1487083
Report Number(s):
FERMILAB-PUB-17-317-A-AE; arXiv:1709.00992; BNL-114383-2017-JA
Journal ID: ISSN 0035-8711; 1621443; TRN: US1900591
Grant/Contract Number:  
AC02-07CH11359; SC0012704; AC05-00OR22725; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Volume: 477; Journal Issue: 2; Journal ID: ISSN 0035-8711
Publisher:
Royal Astronomical Society
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; galaxies: distances and redshifts; cosmology: observations

Citation Formats

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., Carnero Rosell, A., Castander, F. J., Chang, C., da Costa, L. N., Gruen, D., Gschwend, J., Hartley, W. G., Lin, H., MacCrann, N., Maia, M. A. G., Ogando, R. L. C., Roodman, A., Sevilla-Noarbe, I., Troxel, M. A., Wechsler, R. H., Asorey, J., Davis, T. M., Glazebrook, K., Hinton, S. R., Lewis, G., Lidman, C., Macaulay, E., Möller, A., O'Neill, C. R., Sommer, N. E., Uddin, S. A., Yuan, F., Zhang, B., Abbott, T. M. C., Allam, S., Annis, J., Bechtol, K., Brooks, D., Burke, D. L., Carollo, D., Carrasco Kind, M., Carretero, J., Cunha, C. E., D'Andrea, C. B., DePoy, D. L., Desai, S., Eifler, T. F., Evrard, A. E., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D. W., Goldstein, D. A., Gruendl, R. A., Gutierrez, G., Honscheid, K., Hoormann, J. K., Jain, B., James, D. J., Jarvis, M., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Krause, E., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Li, T. S., Lima, M., Marshall, J. L., Melchior, P., Menanteau, F., Nichol, R. C., Nord, B., Plazas, A. A., Reil, K., Rykoff, E. S., Sako, M., Sanchez, E., Scarpine, V., Schubnell, M., Sheldon, E., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, B. E., Tucker, D. L., Vikram, V., Walker, A. R., Weller, J., Wester, W., and Wolf, R. C. Dark Energy Survey Year 1 results: cross-correlation redshifts – methods and systematics characterization. United States: N. p., 2018. Web. doi: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., Carnero Rosell, A., Castander, F. J., Chang, C., da Costa, L. N., Gruen, D., Gschwend, J., Hartley, W. G., Lin, H., MacCrann, N., Maia, M. A. G., Ogando, R. L. C., Roodman, A., Sevilla-Noarbe, I., Troxel, M. A., Wechsler, R. H., Asorey, J., Davis, T. M., Glazebrook, K., Hinton, S. R., Lewis, G., Lidman, C., Macaulay, E., Möller, A., O'Neill, C. R., Sommer, N. E., Uddin, S. A., Yuan, F., Zhang, B., Abbott, T. M. C., Allam, S., Annis, J., Bechtol, K., Brooks, D., Burke, D. L., Carollo, D., Carrasco Kind, M., Carretero, J., Cunha, C. E., D'Andrea, C. B., DePoy, D. L., Desai, S., Eifler, T. F., Evrard, A. E., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D. W., Goldstein, D. A., Gruendl, R. A., Gutierrez, G., Honscheid, K., Hoormann, J. K., Jain, B., James, D. J., Jarvis, M., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Krause, E., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Li, T. S., Lima, M., Marshall, J. L., Melchior, P., Menanteau, F., Nichol, R. C., Nord, B., Plazas, A. A., Reil, K., Rykoff, E. S., Sako, M., Sanchez, E., Scarpine, V., Schubnell, M., Sheldon, E., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, B. E., Tucker, D. L., Vikram, V., Walker, A. R., Weller, J., Wester, W., & Wolf, R. C. Dark Energy Survey Year 1 results: cross-correlation redshifts – methods and systematics characterization. United States. https://doi.org/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., Carnero Rosell, A., Castander, F. J., Chang, C., da Costa, L. N., Gruen, D., Gschwend, J., Hartley, W. G., Lin, H., MacCrann, N., Maia, M. A. G., Ogando, R. L. C., Roodman, A., Sevilla-Noarbe, I., Troxel, M. A., Wechsler, R. H., Asorey, J., Davis, T. M., Glazebrook, K., Hinton, S. R., Lewis, G., Lidman, C., Macaulay, E., Möller, A., O'Neill, C. R., Sommer, N. E., Uddin, S. A., Yuan, F., Zhang, B., Abbott, T. M. C., Allam, S., Annis, J., Bechtol, K., Brooks, D., Burke, D. L., Carollo, D., Carrasco Kind, M., Carretero, J., Cunha, C. E., D'Andrea, C. B., DePoy, D. L., Desai, S., Eifler, T. F., Evrard, A. E., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D. W., Goldstein, D. A., Gruendl, R. A., Gutierrez, G., Honscheid, K., Hoormann, J. K., Jain, B., James, D. J., Jarvis, M., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Krause, E., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Li, T. S., Lima, M., Marshall, J. L., Melchior, P., Menanteau, F., Nichol, R. C., Nord, B., Plazas, A. A., Reil, K., Rykoff, E. S., Sako, M., Sanchez, E., Scarpine, V., Schubnell, M., Sheldon, E., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, B. E., Tucker, D. L., Vikram, V., Walker, A. R., Weller, J., Wester, W., and Wolf, R. C. Thu . "Dark Energy Survey Year 1 results: cross-correlation redshifts – methods and systematics characterization". United States. https://doi.org/10.1093/mnras/sty466. https://www.osti.gov/servlets/purl/1439283.
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@article{osti_1439283,
title = {Dark Energy Survey Year 1 results: cross-correlation redshifts – methods and systematics characterization},
author = {Gatti, M. and Vielzeuf, P. and Davis, C. and Cawthon, R. and Rau, M. M. and DeRose, J. and De Vicente, J. and Alarcon, A. and Rozo, E. and Gaztanaga, E. and Hoyle, B. and Miquel, R. and Bernstein, G. M. and Bonnett, C. and Carnero Rosell, A. and Castander, F. J. and Chang, C. and da Costa, L. N. and Gruen, D. and Gschwend, J. and Hartley, W. G. and Lin, H. and MacCrann, N. and Maia, M. A. G. and Ogando, R. L. C. and Roodman, A. and Sevilla-Noarbe, I. and Troxel, M. A. and Wechsler, R. H. and Asorey, J. and Davis, T. M. and Glazebrook, K. and Hinton, S. R. and Lewis, G. and Lidman, C. and Macaulay, E. and Möller, A. and O'Neill, C. R. and Sommer, N. E. and Uddin, S. A. and Yuan, F. and Zhang, B. and Abbott, T. M. C. and Allam, S. and Annis, J. and Bechtol, K. and Brooks, D. and Burke, D. L. and Carollo, D. and Carrasco Kind, M. and Carretero, J. and Cunha, C. E. and D'Andrea, C. B. and DePoy, D. L. and Desai, S. and Eifler, T. F. and Evrard, A. E. and Flaugher, B. and Fosalba, P. and Frieman, J. and García-Bellido, J. and Gerdes, D. W. and Goldstein, D. A. and Gruendl, R. A. and Gutierrez, G. and Honscheid, K. and Hoormann, J. K. and Jain, B. and James, D. J. and Jarvis, M. and Jeltema, T. and Johnson, M. W. G. and Johnson, M. D. and Krause, E. and Kuehn, K. and Kuhlmann, S. and Kuropatkin, N. and Li, T. S. and Lima, M. and Marshall, J. L. and Melchior, P. and Menanteau, F. and Nichol, R. C. and Nord, B. and Plazas, A. A. and Reil, K. and Rykoff, E. S. and Sako, M. and Sanchez, E. and Scarpine, V. and Schubnell, M. and Sheldon, E. and Smith, M. and Smith, R. C. and Soares-Santos, M. and Sobreira, F. and Suchyta, E. and Swanson, M. E. C. and Tarle, G. and Thomas, D. and Tucker, B. E. and Tucker, D. L. and Vikram, V. and Walker, A. R. and Weller, J. and Wester, W. and Wolf, R. C.},
abstractNote = {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.},
doi = {10.1093/mnras/sty466},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 477,
place = {United States},
year = {2018},
month = {2}
}
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Figures / Tables:

Figure 1 Figure 1: True redshift distributions for the simulated weak lensing source samples obtained binning with different photo-z codes, as described in §3.2. The redshift distributions are normalized to unity over the full redshift interval.
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text, January 2014

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text, January 2009

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text, January 2010

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text, January 2011

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text, January 2012

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text, January 2012

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text, January 2013

redMaPPer I: Algorithm and SDSS DR8 Catalog
text, January 2013

On the complementarity of galaxy clustering with cosmic shear and flux magnification
text, January 2013

Accurate photometric redshift probability density estimation - method comparison and application
preprint, January 2015

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text, January 2015

DNF - Galaxy photometric redshift by Directional Neighbourhood Fitting
text, January 2015

Clustering-based redshift estimation: application to VIPERS/CFHTLS
text, January 2016

The-wiZZ: Clustering redshift estimation for everyone
text, January 2016

2dFLenS and KiDS: Determining source redshift distributions with cross-correlations
text, January 2016

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text, January 2004

The DEEP2 Galaxy Redshift Survey: Clustering of Galaxies as a Function of Luminosity at z=1
text, January 2005

The Scale Dependence of Halo and Galaxy Bias: Effects in Real Space
text, January 2006

Bayesian photometric redshift estimation
text, January 1998

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    Works referencing / citing this record:

    Dark Energy Survey Year 1 Results: A Precise H0 Estimate from DES Y1, BAO, and D/H Data
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    text, January 2018

    • Cawthon, R.; Davis, C.; Gatti, M.
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    Dark Energy Survey Year 1 Results: A Precise H0 Measurement from DES Y1, BAO, and D/H Data
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    Cosmological Constraints from Multiple Probes in the Dark Energy Survey
    text, January 2018

    Producing a BOSS-CMASS sample with DES imaging
    text, January 2019

    Optimizing galaxy samples for clustering measurements in photometric surveys
    text, January 2019

    Corrfunc --- A Suite of Blazing Fast Correlation Functions on the CPU
    text, January 2019

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      Figures / Tables found in this record:

      Figure 1 (p. 7)figure
      Figure 2 (p. 7)figure
      Figure 3 (p. 7)figure
      Figure 4 (p. 9)figure
      Table 1. BPZ systematic errors (p. 10)table
      Table 2. DNF systematic errors (p. 10)table
      Figure 5 (p. 12)figure
      Figure 6 (p. 12)figure
      Figure 7 (p. 14)figure
      Table 3 (p. 14)table
      Figure 8 (p. 15)figure
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