Skip to main content

Thank you for visiting 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.

The challenge of blending in large sky surveys


The increasing sensitivity of modern sky surveys allow ever fainter emissions of light to be detected, but it also increases the chances of noticeable overlap between multiple sources of light, a phenomenon called blending. The consequences of blending are expected to be among the leading systematic measurement uncertainties of future surveys, such as the Legacy Survey of Space and Time. This Perspective discusses two main approaches to addressing blending: attempting to separate individual sources and statistically correcting for the presence of blending at the population level. For both approaches, simultaneous access to data of multiple surveys will be critical to construct a joint data set that combines the strengths of each individual survey.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: The same sky region of 1.5 × 0.75 arcmin2, observed by different surveys.


  1. 1.

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

    ADS  Article  Google Scholar 

  2. 2.

    Aihara, H. et al. The Hyper Suprime-Cam SSP survey: overview and survey design. Publ. Astron. Soc. Jpn. 70, S4 (2018).

    Google Scholar 

  3. 3.

    Ivezić, Ž. et al. LSST: from science drivers to reference design and anticipated data products. Preprint at arXiv (2008).

  4. 4.

    Sanchez, J., Mendoza, I., Kirkby, D. P. & Burchat, P. R. Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias. Preprint at arXiv (2021).

  5. 5.

    Bosch, J. et al. The Hyper Suprime-Cam software pipeline. Publ. Astron. Soc. Jpn. 70, S5 (2018).

    Article  Google Scholar 

  6. 6.

    Samuroff, S. et al. Dark energy survey year 1 results: the impact of galaxy neighbours on weak lensing cosmology with IM3SHAPE. Mon. Not. R. Astron. Soc. 475, 4524–4543 (2018).

    ADS  Article  Google Scholar 

  7. 7.

    Huang, S. et al. Characterization and photometric performance of the Hyper Suprime-Cam software pipeline. Publ. Astron. Soc. Jpn. 70, S6 (2018).

    Article  Google Scholar 

  8. 8.

    Chang, C. et al. The effective number density of galaxies for weak lensing measurements in the LSST project. Mon. Not. R. Astron. Soc. 434, 2121–2135 (2013).

    ADS  Article  Google Scholar 

  9. 9.

    Hartlap, J., Hilbert, S., Schneider, P. & Hildebrandt, H. A bias in cosmic shear from galaxy selection: results from ray-tracing simulations. Astron. Astrophys. 528, A51 (2011).

    ADS  Article  Google Scholar 

  10. 10.

    Dawson, W. A., Schneider, M. D., Anthony Tyson, J. & James Jee, M. The ellipticity distribution of ambiguously blended objects. Astrophys. J. 816, 11 (2015).

    ADS  Article  Google Scholar 

  11. 11.

    Hoekstra, H., Viola, M. & Herbonnet, R. A study of the sensitivity of shape measurements to the input parameters of weak-lensing image simulations. Mon. Not. R. Astron. Soc. 468, 3295–3311 (2017).

    ADS  Article  Google Scholar 

  12. 12.

    Martinet, N. et al. Euclid preparation - IV. impact of undetected galaxies on weak-lensing shear measurements. Astron. Astrophys. 627, A59 (2019).

    Article  Google Scholar 

  13. 13.

    Melchior, P. & Goulding, A. D. Filling the gaps: Gaussian mixture models from noisy, truncated or incomplete samples. Astron. Comput. 25, 183–194 (2018).

    ADS  Article  Google Scholar 

  14. 14.

    Calzetti, D. The dust opacity of star-forming galaxies. Publ. Astron. Soc. Pac. 113, 1449 (2001).

    ADS  Article  Google Scholar 

  15. 15.

    Stetson, P. B. DAOPHOT: a computer program for crowded-field stellar photometry. Publ. Astron. Soc. Pac. 99, 191 (1987).

    ADS  Article  Google Scholar 

  16. 16.

    Linde, P. High precision crowded field photometry. Highlights Astron. 8, 651–656 (1989).

    ADS  Article  Google Scholar 

  17. 17.

    Feder, R. M., Portillo, S. K. N., Daylan, T. & Finkbeiner, D. Multiband probabilistic cataloging: a joint fitting approach to point-source detection and deblending. Astron. J. 159, 163 (2020).

    ADS  Article  Google Scholar 

  18. 18.

    Hubble, E. P. Extragalactic nebulae. Astrophys. J. 64, 321–369 (1926).

    ADS  Article  Google Scholar 

  19. 19.

    Sérsic, J. L. Influence of the atmospheric and instrumental dispersion on the brightness distribution in a galaxy. Bol. Asoc. Argent. Astron. Plata Argent. 6, 41–43 (1963).

    ADS  Google Scholar 

  20. 20.

    Spergel, D. N. Analytical galaxy profiles for photometric and lensing analysis. Astrophys. J. Suppl. Ser. 191, 58 (2010).

    ADS  Article  Google Scholar 

  21. 21.

    Hogg, D. W. & Lang, D. Replacing standard galaxy profiles with mixtures of Gaussians. Publ. Astron. Soc. Pac. 125, 719 (2013).

    ADS  Article  Google Scholar 

  22. 22.

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

    ADS  Article  Google Scholar 

  23. 23.

    Jarvis, M. et al. The DES science verification weak lensing shear catalogues. Mon. Not. R. Astron. Soc. 460, 2245–2281 (2016).

    ADS  Article  Google Scholar 

  24. 24.

    Drlica-Wagner, A. et al. Dark energy survey year 1 results: The photometric data set for cosmology. Astrophys. J. Suppl. Ser. 235, 33 (2018).

    ADS  Article  Google Scholar 

  25. 25.

    Stoughton, C., Lupton, R. H., Bernardi, M., Blanton, M. R. & Burles, S. Sloan digital sky survey: early data release. Astron. J. 123, 485–548 (2002).

    ADS  Article  Google Scholar 

  26. 26.

    Paatero, P. & Tapper, U. Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111–126 (1994).

    Article  Google Scholar 

  27. 27.

    Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999).

    ADS  MATH  Article  Google Scholar 

  28. 28.

    Melchior, P. et al. Scarlet: source separation in multi-band images by constrained matrix factorization. Astron. Comput. 24, 129–142 (2018).

    ADS  Article  Google Scholar 

  29. 29.

    Melchior, P., Joseph, R. & Moolekamp, F. Proximal Adam: robust adaptive update scheme for constrained optimization. Preprint at arXiv (2019).

  30. 30.

    Hinton, G. E. & Zemel, R. in Advances in Neural Information Processing Systems Vol. 6 (eds Cowan, J. D., Tesauro, G. & Alspector, J.) 3–10 (Morgan-Kaufmann, 1994).

  31. 31.

    Kingma, D. P. & Welling, M. Auto-encoding variational bayes. Preprint at arxiv (2013).

  32. 32.

    Arcelin, B., Doux, C., Aubourg, E. & Roucelle, C. Deblending galaxies with variational autoencoders: a joint multiband, multi-instrument approach. Mon. Not. R. Astron. Soc. 500, 531–547 (2021).

    ADS  Article  Google Scholar 

  33. 33.

    Ronneberger, O., Fischer, P. & Brox, T. in International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234–241 (Springer, 2015).

  34. 34.

    Boucaud, A. et al. Photometry of high-redshift blended galaxies using deep learning. Mon. Not. R. Astron. Soc. 491, 2481–2495 (2020).

    ADS  Article  Google Scholar 

  35. 35.

    Lanusse, F. et al. Deep generative models for galaxy image simulations. Mon. Not. R. Astron. Soc. 504, 5543–5555 (2021).

    ADS  Article  Google Scholar 

  36. 36.

    Salimans, T., Karpathy, A., Chen, X. & Kingma, D. P. PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications. Preprint at arXiv (2017).

  37. 37.

    Lanusse, F., Melchior, P. & Moolekamp, F. Hybrid physical-deep learning model for astronomical inverse problems. Preprint at arXiv (2019).

  38. 38.

    Kaiser, N. Addition of images with varying seeing. Technical report (2004).

  39. 39.

    Zackay, B. & Ofek, E. O. How to COAAD images. I. Optimal source detection and photometry of point sources using ensembles of images. Astrophys. J. 836, 187 (2017).

    ADS  Article  Google Scholar 

  40. 40.

    Daylan, T., Portillo, S. K. N. & Finkbeiner, D. P. Inference of unresolved point sources at high galactic latitudes using probabilistic catalogs. Astrophys. J. 839, 4 (2017).

    ADS  Article  Google Scholar 

  41. 41.

    Liu, R., McAuliffe, J. D. & Regier, J. Variational inference for deblending crowded starfields. Preprint at arXiv (2021).

  42. 42.

    He, K., Gkioxari, G., Dollár, P. & Girshick, R. Mask R-CNN. Preprint at arXiv (2017).

  43. 43.

    Burke, C. J. et al. Deblending and classifying astronomical sources with Mask R-CNN deep learning. Mon. Not. R. Astron. Soc. 490, 3952–3965 (2019).

    ADS  Article  Google Scholar 

  44. 44.

    Vaisanen, P., Tollestrup, E. V. & Fazio, G. G. Confusion limit resulting from galaxies: using the infrared array camera on board SIRTF. Mon. Not. R. Astron. Soc. 325, 1241–1252 (2001).

    ADS  Article  Google Scholar 

  45. 45.

    Kamath, S. Challenges for dark energy science: color gradients and blended objects. PhD thesis, Stanford Univ (2020).

  46. 46.

    Hausen, R. & Robertson, B. E. Morpheus: a deep learning framework for the pixel-level analysis of astronomical image data. Astrophys. J. Suppl. Ser. 248, 20 (2020).

    ADS  Article  Google Scholar 

  47. 47.

    Jones, D. M. & Heavens, A. F. Bayesian photometric redshifts of blended sources. Mon. Not. R. Astron. Soc. 483, 2487–2505 (2019).

    ADS  Article  Google Scholar 

  48. 48.

    Joseph, R., Courbin, F. & L. Starck, J. Multi-band morpho-spectral component analysis deblending tool (MuSCADeT): deblending colourful objects. Astron. Astrophys. Suppl. Ser. 589, A2 (2016).

    Article  Google Scholar 

  49. 49.

    Bryant, J. J. et al. The SAMI galaxy survey: instrument specification and target selection. Mon. Not. R. Astron. Soc. 447, 2857–2879 (2015).

    ADS  Article  Google Scholar 

  50. 50.

    Bundy, K. et al. Overview of the SDSS-IV MaNGA survey: mapping nearby galaxies at Apache Point Observatory. Astrophys. J. 798, 7 (2015).

    ADS  Article  Google Scholar 

  51. 51.

    Johnston, E. J. et al. SDSS-IV MaNGA: bulge–disc decomposition of IFU data cubes (BUDDI). Mon. Not. R. Astron. Soc. 465, 2317–2341 (2017).

    ADS  Article  Google Scholar 

  52. 52.

    Hopkins, P. F. et al. FIRE-2 simulations: physics versus numerics in galaxy formation. Mon. Not. R. Astron. Soc. 480, 800–863 (2018).

    ADS  Article  Google Scholar 

  53. 53.

    Kado-Fong, E., Kim, J.-G., Ostriker, E. C. & Kim, C.-G. Diffuse ionized gas in simulations of multiphase, star-forming galactic disks. Astrophys. J. 897, 143 (2020).

    ADS  Article  Google Scholar 

  54. 54.

    Kim, W.-T., Kim, C.-G. & Ostriker, E. C. Local simulations of spiral galaxies with the TIGRESS framework. I. Star formation and arm spurs/feathers. Astrophys. J. 898, 35 (2020).

    ADS  Article  Google Scholar 

  55. 55.

    Korytov, D. et al. CosmoDC2: A synthetic sky catalog for dark energy science with LSST. Astrophys. J. Suppl. Ser. 245, 26 (2019).

    ADS  Article  Google Scholar 

  56. 56.

    The LSST Dark Energy Science Collaboration et al. The LSST DESC DC2 simulated sky survey. Astrophys. J. Suppl. Ser. 253, 31 (2020).

    Google Scholar 

  57. 57.

    Potter, D., Stadel, J. & Teyssier, R. PKDGRAV3: beyond trillion particle cosmological simulations for the next era of galaxy surveys. Comput. Astrophys. Cosmol. 4, 2 (2017).

    ADS  Article  Google Scholar 

  58. 58.

    Troxel, M. A. et al. A synthetic Roman Space Telescope High-Latitude Imaging Survey: simulation suite and the impact of wavefront errors on weak gravitational lensing. Mon. Not. R. Astron. Soc. 501, 2044–2070 (2021).

    ADS  Article  Google Scholar 

  59. 59.

    Torrey, P. et al. Synthetic galaxy images and spectra from the Illustris simulation. Mon. Not. R. Astron. Soc. 447, 2753–2771 (2015).

    ADS  Article  Google Scholar 

  60. 60.

    Suchyta, E. et al. No galaxy left behind: accurate measurements with the faintest objects in the dark energy survey. Mon. Not. R. Astron. Soc. 457, 786–808 (2016).

    ADS  Article  Google Scholar 

  61. 61.

    Everett, S. et al. Dark energy survey year 3 results: measuring the survey transfer function with Balrog. Preprint at arXiv (2020).

  62. 62.

    Shipley, H. et al. HFF-DeepSpace Photometric Catalogs of the 12 Hubble Frontier Fields, Clusters, and Parallels: Photometry, Photometric Redshifts, and Stellar Masses. Astrophys. J. Suppl. Ser. 235, 14 (2018).

    ADS  MathSciNet  Article  Google Scholar 

  63. 63.

    Eckert, K. et al. Noise from undetected sources in dark energy survey images. Mon. Not. R. Astron. Soc. 497, 2529–2539 (2020).

    ADS  Article  Google Scholar 

  64. 64.

    Gruen, D. et al. Dark energy survey year 1 results: the effect of intracluster light on photometric redshifts for weak gravitational lensing. Mon. Not. R. Astron. Soc. 488, 4389–4399 (2019).

    ADS  Article  Google Scholar 

  65. 65.

    Kannawadi, A. et al. Towards emulating cosmic shear data: revisiting the calibration of the shear measurements for the Kilo-Degree Survey. Astron. Astrophys. 624, A92 (2019).

    Article  Google Scholar 

  66. 66.

    Sheldon, E. S., Becker, M. R., MacCrann, N. & Jarvis, M. Mitigating shear-dependent object detection biases with metacalibration. Astrophys. J. 902, 138 (2020).

    ADS  Article  Google Scholar 

  67. 67.

    Mandelbaum, R. et al. Weak lensing shear calibration with simulations of the HSC survey. Mon. Not. R. Astron. Soc. 481, 3170–3195 (2018).

    ADS  Article  Google Scholar 

  68. 68.

    Kacprzak, T. et al. Measurement and calibration of noise bias in weak lensing galaxy shape estimation. Mon. Not. R. Astron. Soc. 427, 2711–2722 (2012).

    ADS  Article  Google Scholar 

  69. 69.

    Huff, E. & Mandelbaum, R. Metacalibration: direct self-calibration of biases in shear measurement. Preprint at arXiv (2017).

  70. 70.

    Sheldon, E. S. & Huff, E. M. Practical weak-lensing shear measurement with metacalibration. Astrophys. J. 841, 24 (2017).

    ADS  Article  Google Scholar 

  71. 71.

    Hoekstra, H., Kannawadi, A. & Kitching, T. D. Accounting for object detection bias in weak gravitational lensing studies. Astron. Astrophys. 646, A124 (2021).

    ADS  Article  Google Scholar 

  72. 72.

    MacCrann, N. et al. DES Y3 results: blending shear and redshift biases in image simulations (2020). Preprint at arXiv (2020).

  73. 73.

    Connor, T. et al. Crowded field galaxy photometry: precision colors in the CLASH clusters. Astrophys. J. 848, 37 (2017).

    ADS  Article  Google Scholar 

  74. 74.

    Greco, J. P. et al. Illuminating low surface brightness galaxies with the Hyper Suprime-Cam survey. Astrophys. J. 857, 104 (2018).

    ADS  Article  Google Scholar 

  75. 75.

    Zhang, Y. et al. Dark energy survey year 1 results: detection of intracluster light at redshift ~0.25. Astrophys. J. 874, 165 (2019).

    ADS  Article  Google Scholar 

  76. 76.

    Palmese, A. et al. Comparing dark energy survey and HST–CLASH observations of the galaxy cluster RXC J2248.7–4431: implications for stellar mass versus dark matter. Mon. Not. R. Astron. Soc. 463, 1486–1499 (2016).

    ADS  Article  Google Scholar 

  77. 77.

    Koekemoer, A. M. et al. The cosmos survey: Hubble space telescope advanced camera for surveys observations and data processing. Astrophys. J. Suppl. Ser. 172, 196–202 (2007).

    ADS  Article  Google Scholar 

  78. 78.

    Scoville, N. et al. COSMOS: Hubble space telescope observations. Astrophys. J. Suppl. Ser. 172, 38–45 (2007).

    ADS  Article  Google Scholar 

  79. 79.

    Merlin, E. et al. T-PHOT version 2.0: Improved algorithms for background subtraction, local convolution, kernel registration, and new options. Astron. Astrophys. 595, A97 (2016).

    Article  Google Scholar 

  80. 80.

    Nyland, K. et al. An application of multi-band forced photometry to one square degree of SERVS: accurate photometric redshifts and implications for future science. Astrophys. J. Suppl. Ser. 230, 9 (2017).

    ADS  Article  Google Scholar 

  81. 81.

    Rhodes, J. et al. Scientific synergy between LSST and Euclid. Astrophys. J. Suppl. Ser. 233, 21 (2017).

    ADS  Article  Google Scholar 

  82. 82.

    Rhodes, J. et al. Cosmological synergies enabled by joint analysis of multi-probe data from WFIRST, Euclid, and LSST. Bull. Am. Astron. Soc. 51, 201 (2019).

    Google Scholar 

  83. 83.

    Chary, R. et al. Joint survey processing of LSST, Euclid and WFIRST: Enabling a broad array of astrophysics and cosmology through pixel level combinations of datasets. Preprint at arXiv (2019).

  84. 84.

    Joseph, R., Melchior, P. and Moolekamp, F. Joint survey processing: combined resampling and convolution for galaxy modelling and deblending. Preprint at arXiv (2021).

  85. 85.

    Strauss, M. A. et al. Spectroscopic target selection in the Sloan Digital Sky Survey: the main galaxy sample. Astron. J. 124, 1810 (2002).

    ADS  Article  Google Scholar 

  86. 86.

    Dawson, K. S. et al. The Baryon oscillation spectroscopic survey of SDSS-III. Astron. J. 145, 10 (2013).

    ADS  Article  Google Scholar 

  87. 87.

    Takada, M. et al. Extragalactic science, cosmology, and Galactic archaeology with the Subaru Prime Focus Spectrograph. Publ. Astron. Soc. Jpn. 66, R1 (2014).

    ADS  Article  Google Scholar 

  88. 88.

    Foley, R. J. et al. LSST observing strategy white paper: LSST observations of WFIRST deep fields. Preprint at arXiv (2018).

  89. 89.

    Koekemoer, A. et al. Ultra deep field science with WFIRST. Bull. Am. Astron. Soc. 51, 550 (2019).

    Google Scholar 

  90. 90.

    Hartley, W. G. et al. Dark energy survey year 3 results: deep field optical + near-infrared images and catalogue. Preprint at arXiv (2020).

  91. 91.

    York, D. G. et al. The Sloan Digital Sky Survey: Technical summary. Astron. J. 120, 1579 (2000).

    ADS  Article  Google Scholar 

  92. 92.

    Dey, A. et al. Overview of the DESI legacy imaging surveys. Astron. J. 157, 168 (2019).

    ADS  Article  Google Scholar 

  93. 93.

    Grogin, N. A. et al. CANDELS: the cosmic assembly near-infrared deep extragalactic legacy survey. Astrophys. J. Suppl. Ser. 197, 35 (2011).

    ADS  Article  Google Scholar 

  94. 94.

    Koekemoer, A. M. et al. CANDELS: the cosmic assembly near-infrared deep extragalactic legacy survey — the Hubble Space Telescope observations, imaging data products, and mosaics. Astrophys. J. Suppl. Ser. 197, 36 (2011).

    ADS  Article  Google Scholar 

Download references


J.S. acknowledges that this document was prepared using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under contract no. DE-AC02-07CH11359.

Author information




The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Peter Melchior.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Physics thanks Dustin Lang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Euclid Deep Fields:

Hubble Legacy Archive:

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Melchior, P., Joseph, R., Sanchez, J. et al. The challenge of blending in large sky surveys. Nat Rev Phys 3, 712–718 (2021).

Download citation


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