Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

The many flavours of photometric redshifts

Nature Astronomy volume 3, pages 212–222 (2019)Cite this article

Subjects

Abstract

Since more than 70 years ago, the colours of galaxies derived from flux measurements at different wavelengths have been used to estimate their cosmological distances. Such distance measurements, called photometric redshifts, are necessary for many scientific projects, ranging from investigations of the formation and evolution of galaxies and active galactic nuclei to precision cosmology. The primary benefit of photometric redshifts is that distance estimates can be obtained relatively cheaply for all sources detected in photometric images. The drawback is that these cheap estimates have low precision compared with resource-expensive spectroscopic ones. The methodology for estimating redshifts has been through several revolutions in recent decades, triggered by increasingly stringent requirements on the photometric redshift accuracy. Here, we review the various techniques for obtaining photometric redshifts, from template-fitting to machine learning and hybrid schemes. We also describe state-of-the-art results on current extragalactic samples and explain how survey strategy choices affect redshift accuracy. We close with a description of the photometric redshift efforts planned for upcoming wide-field surveys, which will collect data on billions of galaxies, aiming to investigate, among other matters, the stellar mass assembly and the nature of dark energy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The colour–redshift mapping principle.
Fig. 2: Photometric redshift technique in a nutshell.
Fig. 3: Accuracy of various photo-z surveys as a function of their depth and number of bands.
Fig. 4: Impact of multi-wavelength coverage and choice of templates.
Fig. 5: Cosmological parameter biases incurred by photo-z inaccuracy.

Similar content being viewed by others

References

  1. Le Fevre, O. et al. The VIMOS VLT deep survey. First epoch VVDS-deep survey: 11 564 spectra with 17.5 IAB 24, and the redshift distribution over 0 ≤ z ≤ 5. Astron. Astrophys. 439, 845–862 (2005).

    Article  ADS  Google Scholar 

  2. Newman, J. A. et al. Spectroscopic needs for imaging dark energy experiments. Astropart. Phys. 63, 81–100 (2015).

    Article  ADS  Google Scholar 

  3. Baum, W. A. Photoelectric determinations of redshifts beyond 0.2 c. Astron. J 62, 6–7 (1957).

    Article  ADS  Google Scholar 

  4. Puschell, J. J., Owen, F. N. & Laing, R. A. Near-infrared photometry of distant radio galaxies: spectral flux distributions and redshift estimates. Astrophys. J. Lett. 257, 57–61 (1982).

    Article  ADS  Google Scholar 

  5. Ilbert, O. et al. Cosmos photometric redshifts with 30-bands for 2-deg. Astrophys. J. Suppl. Series 189, 1236–1249 (2009).

    Article  ADS  Google Scholar 

  6. Fontana, A. et al. photometric redshifts and selection of high-redshift galaxies in the NTT and Hubble deep fields. Astron. J 120, 2206–2219 (2000).

    Article  ADS  Google Scholar 

  7. Dunlop, J. S. et al. A critical analysis of the ultraviolet continuum slopes (beta) of high-redshift galaxies: no evidence (yet) for extreme stellar populations at z > 6. Mon. Not. R. Astron. Soc. 420, 901–912 (2012).

    Article  ADS  Google Scholar 

  8. Coupon, J. et al. The galaxy-halo connection from a joint lensing, clustering and abundance analysis in the CFHTLenS/VIPERS field. Mon. Not. R. Astron. Soc. 449, 1352–1379 (2015).

    Article  ADS  Google Scholar 

  9. Finoguenov, A. et al. The XMM-Newton Wide-Field Survey in the COSMOS field: statistical properties of clusters of galaxies. Astrophys. J. Suppl. Series 172, 182–195 (2007).

    Article  ADS  Google Scholar 

  10. López-Sanjuan, C. et al. The dominant role of mergers in the size evolution of massive early-type galaxies since z ~ 1. Astron. Astrophys. 548, A7 (2012).

    Article  Google Scholar 

  11. Man, A. W. S., Zirm, A. W. & Toft, S. Resolving the discrepancy of galaxy merger fraction measurements at z ~ 0–3. Astrophys. J. 830, 11–25 (2016).

    Article  ADS  Google Scholar 

  12. Etherington, J. et al. Environmental dependence of the galaxy stellar mass function in the Dark Energy Survey science verification data. Mon. Not. R. Astron. Soc. 466, 228–247 (2017).

    Article  ADS  Google Scholar 

  13. Etherington, J. & Thomas, D. Measuring galaxy environments in large-scale photometric surveys. Mon. Not. R. Astron. Soc. 451, 660–679 (2015).

    Article  ADS  Google Scholar 

  14. Malavasi, N., Pozzetti, L., Cucciati, O., Bardelli, S. & Cimatti, A. Reconstructing the galaxy density field with photometric redshifts. I. Methodology and validation on stellar mass functions. Astron. Astrophys. 585, 116 (2016).

    Article  ADS  Google Scholar 

  15. Miyaji, T. et al. Detailed shape and evolutionary behavior of the X-ray luminosity function of active galactic nuclei. Astrophys. J. 804, 104–129 (2015).

    Article  ADS  Google Scholar 

  16. Padovani, P., Giommi, P. & Rau, A. The discovery of high-power high synchrotron peak blazar. Mon. Not. R. Astron. Soc. 422, 48–52 (2012).

    Article  ADS  Google Scholar 

  17. Hu, W. Power spectrum tomography with weak lensing. Astrophys. J. Lett 522, 21–24 (1999).

    Article  ADS  Google Scholar 

  18. Abbott, T. et al. The Dark Energy Survey: more than dark energy—an overview. Mon. Not. R. Astron. Soc. 460, 1270–1299 (2016).

    Article  ADS  Google Scholar 

  19. Aihara, H. et al. The Hyper Suprime-Cam SSP Survey: overview and survey design. Publ. Astron. Soc. Japan 70, (2018).

  20. Laureijs, R. et al. Euclid Definition Study Report. Preprint at https://arxiv.org/abs/1110.3193 (2011).

  21. Ivezic, Z. et al. LSST: from science drivers to reference design and anticipated data products. Preprint at https://arxiv.org/abs/0805.2366 (2008).

  22. Benitez, N. et al. Optimal filter systems for photometric redshift estimation. Astrophys. J. Lett. 692, 5–8 (2009).

    Article  ADS  Google Scholar 

  23. Fioc, M. & Rocca-Volmernage, B. PEGASE: a UV to NIR spectral evolution model of galaxies. Application to the calibration of bright galaxy counts. Astron. Astrophys. 326, 950–962 (1997).

    ADS  Google Scholar 

  24. Bruzual, G. & Charlot, S. Stellar population synthesis at the resolution of 2003. Mon. Not. R. Astron. Soc. 344, 1000–1028 (2003).

    Article  ADS  Google Scholar 

  25. Maraston, C. Evolutionary population synthesis: models, analysis of the ingredients and application to high-z galaxies. Mon. Not. R. Astron. Soc. 362, 799–825 (2005).

    Article  ADS  Google Scholar 

  26. Conroy, C. On the birth masses of the ancient globular clusters. Astrophys. J. 758, 21–34 (2012).

    Article  ADS  Google Scholar 

  27. Coleman, G. D., Wu, C.-C. & Weedman, D. W. Colors and magnitudes predicted for high redshift galaxies. Astrophys. J. Suppl. Series 43, 393–416 (1980).

    Article  ADS  Google Scholar 

  28. Kinney, A. L. et al. Template ultraviolet to near-infrared spectra of star-forming galaxies and their application to k-corrections. Astrophys. J. 467, 38 (1996).

    Article  ADS  Google Scholar 

  29. Polletta, M. et al. Spectral energy distributions of hard X-ray selected active galactic nuclei in the XMM-Newton Medium Deep Survey. Astrophys. J. 663, 81–102 (2007).

    Article  ADS  Google Scholar 

  30. Noll, S. et al. The FORS Deep Field spectroscopic survey. Astron. Astrophys. 418, 885–906 (2004).

    Article  ADS  Google Scholar 

  31. Chevallard, J. & Charlot, S. Erratum: Modelling and interpreting spectral energy distributions of galaxies with BEAGLE. Mon. Not. R. Astron. Soc. 464, 2349 (2017).

    Article  ADS  Google Scholar 

  32. Ilbert, O. et al. Accurate photometric redshifts for the CFHT legacy survey calibrated using the VIMOS VLT deep survey. Astron. Astrophys. 457, 841–856 (2006).

    Article  ADS  Google Scholar 

  33. Schaerer, D. & de Barros, S. in The Spectral Energy Distribution of Galaxies (eds Tuffs, R. J. & Popescu, C. C.) IAU Symp. 284, 20 (IAU, 2012).

  34. Pacifici, C., Charlot, S., Blaizot, J. & Brinchmann, J. Relative merits of different types of rest-frame optical observations to constrain galaxy physical parameters. Mon. Not. R. Astron. Soc. 421, 2002–2024 (2012).

    Article  ADS  Google Scholar 

  35. Calzetti, D. et al. The dust content and opacity of actively star-forming galaxies. Astrophys. J. 533, 682–695 (2000).

    Article  ADS  Google Scholar 

  36. Prevot, M. L., Lequeux, J., Prevot, L., Maurice, E. & Rocca-Volmerange, E. The typical interstellar extinction in the Small Magellanic Cloud. Astron. Astrophys. 132, 389–392 (1984).

    ADS  Google Scholar 

  37. Madau, P. Radiative transfer in a clumpy universe: the colors of high-redshift galaxies. Astrophys. J. 441, 18–27 (1995).

    Article  ADS  Google Scholar 

  38. Draine, B. T. Physics of the Interstellar and Intergalactic Medium. (Princeton Univ. Press, Princeton, 2011).

    MATH  Google Scholar 

  39. Schlegel, D. J., Finkbeiner, D. P. & Davis, M. Maps of dust infrared emission for use in estimation of reddening and cosmic microwave background radiation foregrounds. Astrophys. J. 500, 525–553 (1998).

    Article  ADS  Google Scholar 

  40. Galametz, A., Saglia, R., Paltani, S., Apostolakos, N. & Dubath, P. SED-dependent galactic extinction prescription for Euclid and future cosmological surveys. Astron. Astrophys. 598, 20 (2017).

    Article  ADS  Google Scholar 

  41. Hildebrandt, H. et al. PHAT: PHoto-z accuracy testing. Astron. Astrophys. 523, 31 (2010).

    Article  Google Scholar 

  42. Cavuoti, S., Brescia, M., Longo, G. & Mercurio, A. Photometric redshifts with the quasi Newton algorithm (MLPQNA) results in the PHAT1 contest. Astron. Astrophys. 546, 13 (2012).

    Article  ADS  Google Scholar 

  43. Beck, R. et al. On the realistic validation of photometric redshift. Mon. Not. R. Astron. Soc. 468, 4323–4339 (2017).

    Article  ADS  Google Scholar 

  44. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  MATH  Google Scholar 

  45. Van Der Malsburg C. In Brain Theory (eds Palm G. & Aertsen A.) 245–248 (Springer, Berlin, 1986); https://doi.org/10.1007/978-3-642-70911-1_20

  46. Carliles, S., Budavári, T., Heinis, S., Priebe, C. & Szalay, A. S. Random forests for photometric redshifts. Astrophys. J. 712, 511–515 (2010).

    Article  ADS  Google Scholar 

  47. Carrasco Kind, M. & Brunner, R. J. TPZ: photometric redshift PDFs and ancillary information by using prediction trees and random forests. Mon. Not. R. Astron. Soc. 432, 1483–1501 (2013).

    Article  ADS  Google Scholar 

  48. Collister, A. A. & Lahav, O. ANNz: estimating photometric redshifts using artificial neural networks. Publ. Astron. Soc. Pacif. 116, 345–351 (2004).

    Article  ADS  Google Scholar 

  49. Vanzella, E. et al. Photometric redshifts with the Multilayer Perceptron Neural Network: application to the HDF-S and SDSS. Astron. Astrophys. 423, 761–776 (2004).

    Article  ADS  Google Scholar 

  50. Brescia, M., Cavuoti, S. & Longo, G. Automated physical classification in the SDSS DR10. A catalogue of candidate quasars. Mon. Not. R. Astron. Soc. 450, 3893–3903 (2015).

    Article  ADS  Google Scholar 

  51. Bonnett, C. Using neural networks to estimate redshift distributions. An application to CFHTLenS. Mon. Not. R. Astron. Soc. 449, 1043–1056 (2015).

    Article  ADS  Google Scholar 

  52. Hoyle, B. Measuring photometric redshifts using galaxy images and Deep Neural Networks. Astron. Comput. 16, 34–40 (2016).

    Article  ADS  Google Scholar 

  53. D'Isanto, A. & Polsterer, K. L. Photometric redshift estimation via deep learning. Astron. Astrophys. 609, 111 (2018).

    Article  ADS  Google Scholar 

  54. Carrasco Kind, M. & Brunner, R. J. SOMz: photometric redshift PDFs with self-organizing maps and random atlas. Mon. Not. R. Astron. Soc. 438, 3409–3421 (2014).

    Article  ADS  Google Scholar 

  55. Masters, D. et al. Mapping the galaxy color–redshift relation: optimal photometric redshift calibration strategies for cosmology surveys. Astrophys. J. 813, 53–68 (2015).

    Article  ADS  Google Scholar 

  56. Cavuoti, S. et al. METAPHOR: A machine-learning-based method for the probability density estimation of photometric redshifts. Mon. Not. R. Astron. Soc. 465, 1959–1973 (2017).

    Article  ADS  Google Scholar 

  57. Hoyle, B. et al. Anomaly detection for machine learning redshifts applied to SDSS galaxies. Mon. Not. R. Astron. Soc. 452, 4183–4194 (2015).

    Article  ADS  Google Scholar 

  58. Hoyle, B., Rau, M. M., Seitz, S. & Weller, J. Data augmentation for machine learning redshifts applied to Sloan Digital Sky Survey galaxies. Mon. Not. R. Astron. Soc. 450, 305–316 (2015).

    Article  ADS  Google Scholar 

  59. Hoyle, B., Rau, M. M., Zitlau, R., Seitz, S. & Weller, J. Feature importance for machine learning redshifts applied to SDSS galaxies. Mon. Not. R. Astron. Soc. 449, 1275–1283 (2015).

    Article  ADS  Google Scholar 

  60. Lima, M. et al. Estimating the redshift distribution of photometric galaxy sample. Mon. Not. R. Astron. Soc. 390, 118–130 (2008).

    Article  ADS  Google Scholar 

  61. Bentez, N. Bayesian photometric redshift estimation. Astrophys. J. 536, 571–583 (2000).

    Article  ADS  Google Scholar 

  62. Dahlen, T. et al. A detailed study of photometric redshifts for GOODS-South galaxies. Astrophys. J. 724, 425–447 (2010).

    Article  ADS  Google Scholar 

  63. Tanaka, M. Photometric redshift with Bayesian priors on physical properties of galaxies. Astrophys. J. 801, 20–39 (2015).

    Article  ADS  Google Scholar 

  64. Seldner, M. & Peebles, P. J. E. Statistical analysis of catalogs of extragalactic objects. XI - Evidence of correlation of QSOs and Lick galaxy counts. Astrophys. J. 227, 30–36 (1979).

    Article  ADS  Google Scholar 

  65. Newman, J. A. Calibrating redshift distributions beyond spectroscopic limits with cross-correlations. Astrophys. J. 684, 88–101 (2008).

    Article  ADS  Google Scholar 

  66. Ménard, B. et al. Clustering-based redshift estimation: method and application to data. Preprint at https://arxiv.org/abs/1303.4722 (2013).

  67. Scottez, V. et al. Clustering-based redshift estimation: application to VIPERS/CFHTLS. Mon. Not. R. Astron. Soc. 462, 1683–1696 (2016).

    Article  ADS  Google Scholar 

  68. Rahman, M., Ménard, B., Scranton, R., Schmidt, C. B. & Morrison, C. B. Clustering-based redshift estimation: comparison to spectroscopic redshifts. Mon. Not. R. Astron. Soc. 447, 3500–3511 (2015).

    Article  ADS  Google Scholar 

  69. Aragon-Calvo, M. A., van de Weygaert, R., Jones, B. J. T. & Mobasher, B. Submegaparsec individual photometric redshift estimation from cosmic web constraints. Mon. Not. R. Astron. Soc. 454, 463–477 (2015).

    Article  ADS  Google Scholar 

  70. Sánchez, C. et al. Photometric redshift analysis in the Dark Energy Survey Science Verification data. Mon. Not. R. Astron. Soc. 445, 1482–1506 (2014).

    Article  ADS  Google Scholar 

  71. Dahlen, T. et al. A critical assessment of photometric redshift methods: a CANDELS investigation. Astrophys. J. 775, 93 (2013).

    Article  ADS  Google Scholar 

  72. Duncan, K. J. et al. Photometric redshifts for the next generation of deep radio continuum surveys—I: Template fitting. Mon. Not. R. Astron. Soc. 473, 2655–2672 (2018).

    Article  ADS  Google Scholar 

  73. Bolzonella, M., Miralles, J.-M. & Pelló, R. Photometric redshifts based on standard SED fitting procedures. Astron. Astrophys. 363, 476–492 (2000).

    ADS  Google Scholar 

  74. Brammer, G. B., van Dokkum, P. G. & Coppi, P. EAZY: a fast, public photometric redshift code. Astrophys. J. 686, 1503–1513 (2008).

    Article  ADS  Google Scholar 

  75. Feldmann, R. et al. The Zurich Extragalactic Bayesian Redshift Analyzer and its first application: COSMOS. Mon. Not. R. Astron. Soc. 372, 565–577 (2006).

    Article  ADS  Google Scholar 

  76. Luo, B. et al. Identifications and photometric redshifts of the 2 ms Chandra Deep Field-South sources. Astrophys. J. Suppl. Series 187, 560–580 (2010).

    Article  ADS  Google Scholar 

  77. Arnouts, S. et al. Measuring and modelling the redshift evolution of clustering: the Hubble Deep Field North. Mon. Not. R. Astron. Soc. 310, 540–556 (1999).

    Article  ADS  Google Scholar 

  78. Hsu, L.-T. et al. CANDELS/GOODS-S, CDFS, and ECDFS: photometric redshifts for normal and X-ray-detected galaxies. Astrophys. J. 796, 60 (2014).

    Article  ADS  Google Scholar 

  79. Cardamone, C. N. et al. The Multiwavelength Survey by Yale-Chile (MUSYC): deep medium-band optical imaging and high-quality 32-band photometric redshifts in the ECDF.-S. Astrophys. J. Suppl. Series 189, 270–285 (2010).

    Article  ADS  Google Scholar 

  80. Pérez-González, P. G. et al. SHARDS: an optical spectro-photometric survey of distant galaxies. Astrophys. J. 762, 46 (2013).

    Article  ADS  Google Scholar 

  81. Molino, A. et al. The ALHAMBRA survey: Bayesian photometric redshifts with 23 bands for 3 deg. Mon. Not. R. Astron. Soc. 441, 2891–2922 (2014).

    Article  ADS  Google Scholar 

  82. Sadeh, I., Abdalla, F. B. & Lahav, O. ANNz2: photometric redshift and probability distribution function estimation using machine learning. Publ. Astron. Soc. Pacif. 128, 104502 (2016).

    Article  ADS  Google Scholar 

  83. Brescia, M. et al. The astronomical data deluge and the template case of photometric redshifts. Preprint at https://arxiv.org/abs/1802.07683 (2018).

  84. Bordoloi, R., Lilly, S. J. & Amara, A. Photo-z performance for precision cosmology. Mon. Not. R. Astron. Soc. 406, 881–895 (2010).

    ADS  Google Scholar 

  85. Bonnett, C. et al. Redshift distributions of galaxies in the Dark Energy Survey Science Verification shear catalogue and implications for weak lensing. Phys. Rev. D 94, 1168 (2016).

    Article  Google Scholar 

  86. Quadri, R. F. & Williams, R. J. Quantifying photometric redshift errors in the absence of spectroscopic redshifts. Astrophys. J. 725, 794–802 (2010).

    Article  ADS  Google Scholar 

  87. Benjamin, J., van Waerbeke, L., Ménard, B. & Kilbinger, M. Photometric redshifts: estimating their contamination and distribution using clustering information. Mon. Not. R. Astron. Soc. 408, 1168–1180 (2010).

    Article  ADS  Google Scholar 

  88. Laigle, C. et al. The COSMOS2015 Catalog: Exploring the 1 < z < 6 Universe with half a million galaxies. Astrophys. J. Suppl. Series 224, 24 (2016).

    Article  ADS  Google Scholar 

  89. Coupon, J. et al. Photometric redshifts for the CFHTLS T0004 deep and wide fields. Astron. Astrophys. 500, 981–998 (2009).

    Article  ADS  Google Scholar 

  90. Wolf, C. et al. A catalogue of the Chandra Deep Field South with multi-colour classification and photometric redshifts from COMBO-17. Astron. Astrophys. 421, 913–936 (2004).

    Article  ADS  Google Scholar 

  91. Cimatti, A. et al. The K20 survey. I. Disentangling old and dusty star-forming galaxies in the ERO population. Astron. Astrophys. 381, 68–72 (2002).

    Article  ADS  Google Scholar 

  92. Bouwens, R. J. et al. UV luminosity functions at redshifts z ~ 4 to z ~ 10: 10,000 galaxies from HST legacy fields. Astrophys. J. 803, 34 (2015).

    Article  ADS  Google Scholar 

  93. Whitaker, K. E. et al. The NEWFIRM medium-band survey: photometric catalogs, redshifts, and the bimodal color distribution of galaxies out to z ~3. Astrophys. J. 735, 86 (2011).

    Article  ADS  Google Scholar 

  94. Drlica-Wagner, A. et al. Dark Energy Survey Year 1 results: photometric data set for cosmology. Preprint at https://arxiv.org/abs/1708.01531 (2017).

  95. Bertin, E. & Arnouts, S. SExtractor: software for source extraction. Astron. Astrophys. Suppl. Series 117, 393–404 (1996).

    Article  ADS  Google Scholar 

  96. Hildebrandt, H. et al. CFHTLenS: improving the quality of photometric redshifts with precision photometry. Mon. Not. R. Astron. Soc. 421, 2355–2367 (2012).

    Article  ADS  Google Scholar 

  97. Moutard, T. et al. The VIPERS Multi-Lambda Survey. I. UV and near-IR observations, multi-colour catalogues, and photometric redshifts. Astron. Astrophys. 590, 102 (2016).

    Article  Google Scholar 

  98. Grazian, A. et al. A comparison of LBGs, DRGs, and BzK galaxies: their contribution to the stellar mass density in the GOODS-MUSIC sample. Astron. Astrophys. 465, 393–404 (2007).

    Article  ADS  Google Scholar 

  99. Mancone, C. L., Gonzales, A. H., Moustakas, L. A. & Price, A. PyGFit: a tool for extracting PSF matched photometry. Publ. Astron. Soc. Pacif. 125, 1514–1524 (2013).

    Article  ADS  Google Scholar 

  100. Bundy, K. et al. SYNMAG photometry: a fast tool for catalog-level matched colors of extended sources. Astron. J. 144, 188 (2012).

    Article  ADS  Google Scholar 

  101. Laidler, V. G. et al. TFIT: a photometry package using prior information for mixed-resolution data sets. Publ. Astron. Soc. Pacif. 119, 1325–1344 (2007).

    Article  ADS  Google Scholar 

  102. Merlin, E. et al. T-PHOT: a new code for PSF-matched, prior-based, multiwavelength extragalactic deconfusion photometry. Astron. Astrophys. 582, 15 (2015).

    Article  Google Scholar 

  103. Magorrian, J. et al. The demography of massive dark objects in galaxy centers. Astron. J 115, 2285–2305 (1998).

    Article  ADS  Google Scholar 

  104. Bongiorno, A. et al. Accreting supermassive black holes in the COSMOS field and the connection to their host galaxies. Mon. Not. R. Astron. Soc. 427, 3103–3133 (2012).

    Article  ADS  Google Scholar 

  105. Salvato, M. et al. Dissecting photometric redshift for active galactic nucleus using XMM- and Chandra-COSMOS samples. Astrophys. J. 742, 61 (2011).

    Article  ADS  Google Scholar 

  106. Marchesi, S. et al. The Chandra COSMOS Legacy survey: optical/IR identifications. Astrophys. J. 817, 34 (2016).

    Article  ADS  Google Scholar 

  107. Zheng, W. et al. Photometric redshift of X-ray sources in the Chandra Deep Field South. Astrophys. J. Suppl. Series 155, 73–87 (2004).

    Article  ADS  Google Scholar 

  108. Brusa, M. et al. The XMM-Newton Wide-field Survey in the Cosmos Field (XMM-COSMOS): demography and multiwavelength properties of obscured and unobscured luminous active galactic nuclei. Astrophys. J. 716, 348–369 (2010).

    Article  ADS  Google Scholar 

  109. Merloni, A. et al. eROSITA Science Book: Mapping the Structure of the Energetic Universe. Preprint at https://arxiv.org/abs/1209.3114 (2012).

  110. Norris, R. et al. EMU: Evolutionary Map of the Universe. Publ. Astron. Soc. Pacif. 28, 215–248 (2011).

    Article  ADS  Google Scholar 

  111. Salvato, M. et al. Photometric redshift and classification for the XMM-COSMOS sources. Astrophys. J. 690, 1250–1263 (2009).

    Article  ADS  Google Scholar 

  112. Kitsionas, S., Hatziminaoglou, E., Georgakakis, A. & Georgantopoulos, I. On the use of photometric redshifts for X-ray selected AGNs. Astron. Astrophys. 434, 475–482 (2005).

    Article  ADS  Google Scholar 

  113. Bovy, J. et al. Photometric redshifts and quasar probabilities from a single, data-driven generative model. Astrophys. J. 749, 41 (2012).

    Article  ADS  Google Scholar 

  114. Brescia, M., Cavuoti, S., D’Abrusco, R., Long, G. & Mercurio, A. Photometric redshifts for quasars in multi-band surveys. Astrophys. J. 772, 140 (2013).

    Article  ADS  Google Scholar 

  115. Budavari, T. et al. Photometric redshifts from reconstructed quasar templates. Astron. J 122, 1163–1161 (2001).

    Article  ADS  Google Scholar 

  116. Mountrichas, E. A. et al. Estimating photometric redshifts for X-ray sources in the X-ATLAS field, using machine-learning techniques. Astron. Astrophys. 608, 39 (2017).

    Article  Google Scholar 

  117. Simm, T. et al. Pan-STARRS1 variability of XMM-COSMOS AGN. I. Impact on photometric redshifts. Astron. Astrophys. 584, 106 (2015).

    Article  Google Scholar 

  118. Rau, A. et al. BL Lacertae objects beyond redshift 1.3—UV-to-NIR photometry and photometric redshift for Fermi/LAT blazars. Astron. Astrophys. 538, A26 (2012).

    Article  Google Scholar 

  119. Krühler, T. et al. Photometric redshifts for gamma-ray burst afterglows from GROND and Swift/UVOT. Astron. Astrophys. 526, 153 (2011).

    Article  Google Scholar 

  120. Palanque-Delabrouille, N. et al. Photometric redshifts for type Ia supernovae in the supernova legacy survey. Astron. Astrophys. 514, 63 (2010).

    Article  Google Scholar 

  121. Lanzetta, K. M., Yahil, A. & Fernández-Soto, A. Star-forming galaxies at very high redshifts. Nature 381, 759–763 (1996).

    Article  ADS  Google Scholar 

  122. Finkelstein, S. et al. The case for a James Webb Space Telescope extragalactic key project. Preprint at https://arxiv.org/abs/1512.04530 (2015).

  123. Bisigello, L. et al. The impact of JWST broadband filter choice on photometric redshift estimation. Astrophys. J. Suppl. Series 227, 19 (2016).

    Article  ADS  Google Scholar 

  124. Schaerer, D. & de Barros, S. On the physical properties of z ~ 6–8 galaxies. Astron. Astrophys. 515, 73 (2010).

    Article  MATH  Google Scholar 

  125. Labbe, I. et al. The spectral energy distributions of z ~ 8 galaxies from the IRAC Ultra Deep Fields: emission lines, stellar masses, and specific star formation rates at 650 Myr. Astrophys. J. Lett. 777, 19–25 (2013).

    Article  ADS  Google Scholar 

  126. Doré, O. et al. Science impacts of the SPHEREx all-sky optical to near-infrared spectral survey: report of a community workshop examining extragalactic, galactic, stellar and planetary science. Preprint at https://arxiv.org/abs/1606.07039 (2016).

  127. Benitez, N. et al. J-PAS: The Javalambre-Physics of the Accelerated Universe Astrophysical Survey. Preprint at https://arxiv.org/abs/1403.5237 (2014).

  128. Tamura, N. et al. Prime Focus Spectrograph (PFS): a very wide-field, massively multi-object, optical and near-infrared fiber-fed spectrograph on the Subaru Telescope. Publ. Astron. Soc. Pacif. 507, 387 (2016).

    Google Scholar 

  129. Carrasco-Kind, M. & Brunner, R. J. Exhausting the information: novel Bayesian combination of photometric redshift PDFs. Mon. Not. R. Astron. Soc. 442, 3380–3399 (2014).

    Article  ADS  Google Scholar 

  130. Leistedt, B. & Hogg, D. W. Data-driven, interpretable photometric redshifts trained on heterogeneous and unrepresentative data. Astrophys. J. 838, 5 (2017).

    Article  ADS  Google Scholar 

  131. Speagle, J. & Eisenstein, D. J. Deriving photometric redshifts using fuzzy archetypes and self-organizing maps—I. Methodology. Mon. Not. R. Astron. Soc. 469, 1186–1204 (2017).

    Article  ADS  Google Scholar 

  132. Duncan, K. J., Jarvis, M. J., Brown, M. J. I. & Rottgering, H. J. A. Photometric redshifts for the next generation of deep radio continuum surveys II. Gaussian processes and hybrid estimates. Preprint at https://arxiv.org/abs/1712.04476 (2018).

  133. Beck, R., Dobos, L., Budavári, T., Szalay, A. S. & Csabai, I. Photometric redshifts for the SDSS Data Release 12. Mon. Not. R. Astron. Soc. 460, 1371–1381 (2016).

    Article  ADS  Google Scholar 

  134. Amendola, L. et al. Cosmology and fundamental physics with the Euclid satellite. Living Rev. Relativ. 16, 6 (2013).

    Article  ADS  Google Scholar 

  135. Bielby, R. et al. The WIRCam Deep Survey. I. Counts, colours, and mass-functions derived from near-infrared imaging in the CFHTLS deep fields. Astron. Astrophys. 545, 23 (2012).

    Article  Google Scholar 

  136. De Jong, J. T. A. et al. The third data release of the Kilo-Degree Survey and associated data products. Astron. Astrophys. 604, 134 (2017).

    Article  Google Scholar 

Download references

Acknowledgements

O.I. acknowledges funding of the French Agence Nationale de la Recherche for the SAGACE project, as well as the financial support received from the Centre National d?Etudes Spatiales for the COSMOS project. We thank L.-T. Hsu, J. Chavez-Montego, P. Gonzalez, H. Hildebrandt, M. Brescia and S. Cavuoti for providing the CANDELS/CDFS, ALHAMBRA, SHARDS, KiDS_BPT and KiDS_MLPQN data that were used in Fig. 3.

Author information

Authors and Affiliations

  1. Max-Planck-Institut für extraterrestrische Physik, Garching, Germany

    Mara Salvato & Ben Hoyle

  2. Laboratoire d’Astrophysique de Marseille, Marseille, France

    Olivier Ilbert

  3. University Sternwarte, Munich, Germany

    Ben Hoyle

Authors
  1. Mara Salvato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Olivier Ilbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ben Hoyle
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed equally to the writing of this Review Article.

Corresponding authors

Correspondence to Mara Salvato, Olivier Ilbert or Ben Hoyle.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salvato, M., Ilbert, O. & Hoyle, B. The many flavours of photometric redshifts. Nat Astron 3, 212–222 (2019). https://doi.org/10.1038/s41550-018-0478-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41550-018-0478-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing