The ellipticity distribution of ambiguously blended objects

William A. Dawson, Michael D. Schneider, J. Anthony Tyson, M. James Jee

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35 Citations (Scopus)


Using overlapping fields with space-based Hubble Space Telescope and ground-based Subaru Telescope imaging we identify a population of blended galaxies that are blended to such a large degree that they are detected as single objects in the ground-based monochromatic imaging, which we label "ambiguous blends." For deep imaging data, such as the depth targeted with the Large Synoptic Survey Telescope (LSST), the ambiguous blend population is both large (∼14%) and has a distribution of ellipticities that is different from that of unblended objects in a way that will likely be important for weak lensing measurements. Most notably, for a limiting magnitude of i ∼ 27 we find that ambiguous blending results in a ∼14% increase in shear noise (or an ∼12% decrease in the effective projected number density of lensed galaxies; neff) due to (1) larger intrinsic ellipticity dispersion, and (2) a scaling with the galaxy number density Ngal that is shallower than 1/√Ngal. For the LSST Gold Sample (i < 25.3) there is a ∼7% increase in shear noise (or ∼7% decrease in neff). More importantly than these increases in the shear noise, we find that the ellipticity distribution of ambiguous blends has an rms that is 13% larger than that of non-blended galaxies. Given the need of future weak lensing surveys to constrain the ellipticity distribution of galaxies to better than a percent in order to mitigate cosmic shear multiplicative biases, if it is unaccounted for, the different ellipticity distribution of ambiguous blends could be a dominant systematic.

Original languageEnglish
Article number11
JournalAstrophysical Journal
Issue number1
Publication statusPublished - 2016 Jan 1

Bibliographical note

Publisher Copyright:
© 2016. The American Astronomical Society. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science


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