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  • Perspective
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The challenge of blending in large sky surveys

Abstract

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.

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

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References

  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).

    Article  ADS  Google Scholar 

  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. Ivezić, Ž. et al. LSST: from science drivers to reference design and anticipated data products. Preprint at arXiv http://arxiv.org/abs/0805.2366 (2008).

  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 http://arxiv.org/abs/2103.02078 (2021).

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

    Article  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  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. 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).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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

    Article  ADS  MATH  Google Scholar 

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

    Article  ADS  Google Scholar 

  29. Melchior, P., Joseph, R. & Moolekamp, F. Proximal Adam: robust adaptive update scheme for constrained optimization. Preprint at arXiv https://ui.adsabs.harvard.edu/abs/2019arXiv191010094M (2019).

  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). https://proceedings.neurips.cc/paper/1993/file/9e3cfc48eccf81a0d57663e129aef3cb-Paper.pdf.

  31. Kingma, D. P. & Welling, M. Auto-encoding variational bayes. Preprint at arxiv http://arxiv.org/abs/1312.6114v10 (2013).

  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).

    Article  ADS  Google Scholar 

  33. Ronneberger, O., Fischer, P. & Brox, T. in International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234–241 (Springer, 2015). https://doi.org/10.1007/978-3-319-24574-4_28.

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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 http://arxiv.org/abs/1701.05517 (2017).

  37. Lanusse, F., Melchior, P. & Moolekamp, F. Hybrid physical-deep learning model for astronomical inverse problems. Preprint at arXiv http://arxiv.org/abs/1912.03980 (2019).

  38. Kaiser, N. Addition of images with varying seeing. Technical report http://pan-starrs.ifa.hawaii.edu/project/people/kaiser/imageprocessing/im++.pdf (2004).

  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).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  41. Liu, R., McAuliffe, J. D. & Regier, J. Variational inference for deblending crowded starfields. Preprint at arXiv http://arxiv.org/abs/2102.02409 (2021).

  42. He, K., Gkioxari, G., Dollár, P. & Girshick, R. Mask R-CNN. Preprint at arXiv http://arxiv.org/abs/1703.06870 (2017).

  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).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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

  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).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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. 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).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  61. Everett, S. et al. Dark energy survey year 3 results: measuring the survey transfer function with Balrog. Preprint at arXiv http://arxiv.org/abs/2012.12825 (2020).

  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).

    Article  ADS  MathSciNet  Google Scholar 

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

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  69. Huff, E. & Mandelbaum, R. Metacalibration: direct self-calibration of biases in shear measurement. Preprint at arXiv http://arxiv.org/abs/1702.02600 (2017).

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  72. MacCrann, N. et al. DES Y3 results: blending shear and redshift biases in image simulations (2020). Preprint at arXiv http://arxiv.org/abs/2012.08567 (2020).

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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. 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).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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. 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 http://arxiv.org/abs/1910.01259 (2019).

  84. Joseph, R., Melchior, P. and Moolekamp, F. Joint survey processing: combined resampling and convolution for galaxy modelling and deblending. Preprint at arXiv https://arxiv.org/abs/2107.06984 (2021).

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  88. Foley, R. J. et al. LSST observing strategy white paper: LSST observations of WFIRST deep fields. Preprint at arXiv http://arxiv.org/abs/1812.00514 (2018).

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

    Google Scholar 

  90. Hartley, W. G. et al. Dark energy survey year 3 results: deep field optical + near-infrared images and catalogue. Preprint at arXiv http://arxiv.org/abs/2012.12824 (2020).

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  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).

    Article  ADS  Google Scholar 

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Acknowledgements

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.

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Euclid Deep Fields: https://www.cosmos.esa.int/web/euclid/euclid-survey

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Melchior, P., Joseph, R., Sanchez, J. et al. The challenge of blending in large sky surveys. Nat Rev Phys 3, 712–718 (2021). https://doi.org/10.1038/s42254-021-00353-y

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