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An example of a realistic galaxy image simulation in GREAT3. Irregular galaxy morphology can be clearly seen.
Source publication
The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is an image
analysis competition that aims to test algorithms to measure weak gravitational
lensing from astronomical images. The challenge started in October 2013 and
ends 30 April 2014. The challenge focuses on testing the impact on weak lensing
measurements of realistically complex...
Context in source publication
Context 1
... the procedure described in [14], the HST PSF is removed in Fourier space, the weak lensing shear and magnification are applied, and then the target PSF is applied. An example of realistic galaxy image is shown in figure 2. Note that PSFs are given in the same way as described in section 3.1. ...
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Citations
... To this purpose, we inject another set of simulated objects into the original HFF mosaics. This time the simulated sources are realistic galaxy models from the GREAT3 challenge (Miyatake et al. 2014), created by means of GalSim . GalSim is a modular, open-source code to render any type of astronomical images. ...
We present a multiband analysis of the six Hubble Frontier Field clusters and their parallel fields, producing catalogs with measurements of source photometry and photometric redshifts. We release these catalogs to the public along with maps of intracluster light and models for the brightest galaxies in each field. This rich data set covers a wavelength range from 0.2 to 8 μ m, utilizing data from the Hubble Space Telescope, Keck Observatories, Very Large Telescope array, and Spitzer Space Telescope. We validate our products by injecting into our fields and recovering a population of synthetic objects with similar characteristics to those in real extragalactic surveys. The photometric catalogs contain a total of over 32,000 entries, with 50% completeness at a threshold of mag AB ∼ 29.1 for unblended sources and mag AB ∼ 29 for blended ones, in the IR-weighted detection band. Photometric redshifts were obtained by means of template fitting and have an average outlier fraction of 10.3% and scatter σ = 0.067 when compared to spectroscopic estimates. The software we devised, after being tested in the present work, will be applied to new data sets from ongoing and future surveys.
... To this purpose, we inject another set of simulated objects into the original HFF mosaics. This time the simulated sources are realistic galaxy models from the GREAT3 challenge (Miyatake et al. 2014), created by means of GalSim . GalSim is a modular, open-source code to render any type of astronomical images. ...
We present a multi-band analysis of the six Hubble Frontier Field clusters and their parallel fields, producing catalogs with measurements of source photometry and photometric redshifts. We release these catalogs to the public along with maps of intracluster light and models for the brightest galaxies in each field. This rich data set covers a wavelength range from 0.2 to 8 $\mu m$, utilizing data from the Hubble Space Telescope, Keck Observatories, Very Large Telescope array, and Spitzer Space Telescope. We validate our products by injecting into our fields and recovering a population of synthetic objects with similar characteristics as in real extragalactic surveys. The photometric catalogs contain a total of over 32,000 entries with 50\% completeness at a threshold of $\mathrm{mag_{AB}}\sim 29.1$ for unblended sources, and $\mathrm{mag_{AB}}\sim 29$ for blended ones, in the IR-Weighted detection band. Photometric redshifts were obtained by means of template fitting and have an average outlier fraction of 10.3\% and scatter $\sigma = 0.067$ when compared to spectroscopic estimates. The software we devised, after being tested in the present work, will be applied to new data sets from ongoing and future surveys.
We present our results from training and evaluating a convolutional neural network (CNN) to predict galaxy shapes from wide-field survey images of the first data release of the Dark Energy Survey (DES DR1). We use conventional shape measurements as ‘ground truth’ from an overlapping, deeper survey with less sky coverage, the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS). We demonstrate that CNN predictions from single band DES images reproduce the results of CFHTLenS at bright magnitudes and show higher correlation with CFHTLenS at fainter magnitudes than maximum likelihood model fitting estimates in the DES Y1 im3shape catalogue. Prediction of shape parameters with a CNN is also extremely fast, it takes only 0.2 ms per galaxy, improving more than 4 orders of magnitudes over forward model fitting. The CNN can also accurately predict shapes when using multiple images of the same galaxy, even in different colour bands, with no additional computational overhead. The CNN is again more precise for faint objects, and the advantage of the CNN is more pronounced for blue galaxies than red ones when compared to the DES Y1 metacalibration catalogue, which fits a single Gaussian profile using riz band images. We demonstrate that CNN shape predictions within the metacalibration self-calibrating framework yield shear estimates with negligible multiplicative bias, m < 10−3, and no significant point spread function (PSF) leakage. Our proposed set-up is applicable to current and next-generation weak lensing surveys where higher quality ‘ground truth’ shapes can be measured in dedicated deep fields.
