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Cited by 354Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and ObservationsCosmology and Gravitation Theories

THE POPULATION OF GALAXY–GALAXY STRONG LENSES IN FORTHCOMING OPTICAL IMAGING SURVEYS

Open access
Collett, Thomas E.·2015·The Astrophysical Journal
DOI 10.1088/0004-637x/811/1/20
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Abstract

Ongoing and future imaging surveys represent significant improvements in depth, area, and seeing compared to current data sets. These improvements offer the opportunity to discover up to three orders of magnitude more galaxy–galaxy strong lenses than are currently known. In this work we forecast the number of lenses that will be discoverable in forthcoming surveys and simulate their properties. We generate a population of statistically realistic strong lenses and simulate observations of this population for the Dark Energy Survey (DES), the Large Synoptic Survey Telescope (LSST), and Euclid surveys. We verify our model against the galaxy-scale lens search of the Canada–France–Hawaii Telescope Legacy Survey, predicting 250 discoverable lenses compared to 220 found by Gavazzi et al. The predicted Einstein radius distribution is also remarkably similar to that found by Sonnenfeld et al. For future surveys we find that, assuming Poisson limited lens galaxy subtraction, searches of the DES, LSST, and Euclid data sets should discover 2400, 120000, and 170000 galaxy–galaxy strong lenses, respectively. Finders using blue-minus-red ( ) difference imaging for lens subtraction can discover 1300 and 62000 lenses in DES and LSST. The uncertainties on the model are dominated by the high-redshift source population, which typically gives fractional errors on the discoverable lens number at the level of tens of percent. We find that doubling the signal-to-noise ratio required for a lens to be detectable approximately halves the number of detectable lenses in each survey, indicating the importance of understanding the selection function and the sensitivity of future lens finders in interpreting strong lens statistics. We make our population forecasting and simulated observation codes publicly available so that the selection function of strong lens finders can easily be calibrated.

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Crossref · 10.1088/0004-637x/811/1/20

Cites

59 of 59 references verified0 citation errors · 0 not checkable

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  • The Einstein radius distribution of galaxy-galaxy lenses in the Universe is expected to peak at 0.4 arcsec.

    “The Einstein radius distribution of galaxy-galaxy lenses in the Universe is expected to peak at 0 . ′′ 4 (Collett 2015)”

    From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Collett 2015

    Section 3 says the lens distribution peaks at approximately 0.5 arcsec, not 0.4 arcsec. Section 2.3 likewise says most lenses in the idealized whole-sky model have Einstein radii around 0.5 arcsec. Page numbers are unavailable in our copy. (LLM, conf=0.88)

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  • Simulations S1 and S3 tend to produce larger Einstein radii than S2, and all simulation sets have larger radii than predicted by the forecast.

    “Simulations S1 and S3 tend to produce larger Einstein radii than S2, and all simulation sets have larger radii than predicted by the forecast.”

    From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Collett 2015

    Section 2.3 (page unavailable) reports the paper’s forecast that most lenses have Einstein radii around 0.5 arcsec. The paper does not identify simulation sets S1, S2, or S3 or compare their radii with one another or with that forecast. (LLM, conf=0.81)

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  • The distributions of lens redshifts from simulations mostly align with the forecast by Collett (2015), while there is much more variation in source redshift distribution.

    “These distributions mostly align for the lens redshifts, while there is much more variation in source redshift distribution.”

    From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Collett 2015

    Section 6.2 (page unavailable) presents Collett’s forecast lens and source redshift distributions, so the paper supplies a basis for the citing authors’ comparison. It does not assess whether other simulations align with its lens-redshift forecast or show greater variation in their source-redshift distributions. Within its own survey forecasts, it instead reports no significant variation in source-redshift distributions. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • The distribution of Einstein radii from Q1 lens modelling aligns reasonably well with the simulations but is shifted to larger radii compared to the forecast.

    “The distribution from Q1 lens modelling aligns reasonably well with the simulations but is shifted to larger radii compared to the forecast.”

    From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Collett 2015

    Section 5 compares modelled SL2S Einstein radii with Collett’s forecast and says the distributions are similar, supporting the general comparison. It does not discuss Q1 lens modelling or say that its distribution is shifted toward larger radii; Figure 3’s caption likewise identifies the comparison as SL2S rather than Q1. No page number is available in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The statistics from the 97 known grade A Euclid lenses and 26,222 non-lens Euclid cutouts were scaled to match the ratio of expected lenses according to Collett (2015).

    “Since the ratio of 97:26 222 does not reflect the proportion of lenses expected in real Euclid data, we scale the statistics to match the ratio of expected lenses according to Collett (2015).”

    From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Collett (2015)

    Section 6 (page not supplied) forecasts 170,000 discoverable lenses in Euclid, supporting the use of Collett (2015) for an expected lens count. The passages do not give a lens-to-non-lens ratio or describe scaling the citing authors’ 97 lens and 26,222 non-lens cutouts; those parts are not supported by this source. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cites

Flagged first, then by citations

Research works printed but not linked: 7

  1. Barone-Nugent, R. L., Wyithe, J. S. B., Trenti, M., et al. 2015, arXiv:1502.03887 · arXiv
  2. Brault, F., & Gavazzi, R. 2014, arXiv:1411.1265 · arXiv
  3. Loncan, L., Almeida, L. B., Bioucas-Dias, J. M., et al. 2015, arXiv:1504.04531 · arXiv
  4. Marshall, P. J., Verma, A., More, A., et al. 2015, arXiv:1504.06148 · arXiv
  5. Mason, C. A., Treu, T., Schmidt, K. B., et al. 2015, arXiv:1502.03795 · arXiv
  6. More, A., Verma, A., Marshall, P. J., et al. 2015, arXiv:1504.05587 · arXiv
  7. Schneider, P., Ehlers, J., & Falco, E. E. 1992 (Berlin: Springer-Verlag)

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Authors
Collett, T. E.
Affiliations
University of Portsmouth (GB)
Corresponding author
Collett, Thomas E.
ORCID
Collett, Thomas E.
Venue
The Astrophysical Journal
Publisher
American Astronomical Society
Published
2015-09-16
Volume
811
Issue
1
Pages
20
ISSN
  • 0004-637X (ISSN-L)
  • 1538-4357
DOI
10.1088/0004-637x/811/1/20
OpenAlex
W1694049600
Cited by
354
References
59
Citations by year
2026: 35 · 2025: 59 · 2024: 44 · 2023: 47 · 2022: 48 · 2021: 37 · 2020: 26 · 2019: 22 · 2018: 14 · 2017: 13 · 2016: 9
FWCI
6.68
Type
journal article
Language
en
Open access
Licence
Locations
  • The Astrophysical Journal (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astrophysical Phenomena and Observations
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
Keywords
strong gravitational lensing, image subtraction, selection function, Dark Energy Survey, LSST, Euclid, signal-to-noise ratio

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