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

By T. Collett. 2015

188 citations recorded by the source corpus.

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 ( g − i ?> ) 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.

Subfields: Astronomy and Astrophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/0088a6e3f02531f655d81b3b673e336999095a0b

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Citations in the corpus

CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding (2017, methodology): “Parameters for these simulations follow Collett (2015) and are based on the LSST observation simulator (Connolly et al. 2010).”
TDCOSMO IV: Hierarchical time-delay cosmography -- joint inference of the Hubble constant and galaxy density profiles (2020, methodology): “…deep, wide-field surveys (such as those enabled by Vera Rubin Observatory, Euclid and the Nancy Grace Roman Observatory) will discover many thousands of lenses of which several hundred will have accurate time delay measurements (see e.g., Oguri & Marshall 2010; Collett 2015; Huber et al. 2019).”
Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning (2019, methodology): “This approach scales well to the expected large number of lenses expected in upcoming surveys (Oguri & Marshall 2010; Treu 2010; Collett 2015).”
H0LiCOW – IX. Cosmographic analysis of the doubly imaged quasar SDSS 1206+4332 and a new measurement of the Hubble constant (2018, background): “…increased recently and is approaching the desired number (Schechter et al. 2017; Lin et al. 2017; Jacobs et al. 2017; Ostrovski et al. 2017; Agnello et al. 2018; Lemon et al. 2018; Treu et al. 2018), quads only represent ∼ 1/6 of all lensed quasars in the sky (Oguri & Marshall 2010; Collett 2015).”