6,286 research outputs found
GREAT3 results I: systematic errors in shear estimation and the impact of real galaxy morphology
We present first results from the third GRavitational lEnsing Accuracy
Testing (GREAT3) challenge, the third in a sequence of challenges for testing
methods of inferring weak gravitational lensing shear distortions from
simulated galaxy images. GREAT3 was divided into experiments to test three
specific questions, and included simulated space- and ground-based data with
constant or cosmologically-varying shear fields. The simplest (control)
experiment included parametric galaxies with a realistic distribution of
signal-to-noise, size, and ellipticity, and a complex point spread function
(PSF). The other experiments tested the additional impact of realistic galaxy
morphology, multiple exposure imaging, and the uncertainty about a
spatially-varying PSF; the last two questions will be explored in Paper II. The
24 participating teams competed to estimate lensing shears to within systematic
error tolerances for upcoming Stage-IV dark energy surveys, making 1525
submissions overall. GREAT3 saw considerable variety and innovation in the
types of methods applied. Several teams now meet or exceed the targets in many
of the tests conducted (to within the statistical errors). We conclude that the
presence of realistic galaxy morphology in simulations changes shear
calibration biases by per cent for a wide range of methods. Other
effects such as truncation biases due to finite galaxy postage stamps, and the
impact of galaxy type as measured by the S\'{e}rsic index, are quantified for
the first time. Our results generalize previous studies regarding sensitivities
to galaxy size and signal-to-noise, and to PSF properties such as seeing and
defocus. Almost all methods' results support the simple model in which additive
shear biases depend linearly on PSF ellipticity.Comment: 32 pages + 15 pages of technical appendices; 28 figures; submitted to
MNRAS; latest version has minor updates in presentation of 4 figures, no
changes in content or conclusion
Transfer learning for radio galaxy classification
In the context of radio galaxy classification, most state-of-the-art neural
network algorithms have been focused on single survey data. The question of
whether these trained algorithms have cross-survey identification ability or
can be adapted to develop classification networks for future surveys is still
unclear. One possible solution to address this issue is transfer learning,
which re-uses elements of existing machine learning models for different
applications. Here we present radio galaxy classification based on a 13-layer
Deep Convolutional Neural Network (DCNN) using transfer learning methods
between different radio surveys. We find that our machine learning models
trained from a random initialization achieve accuracies comparable to those
found elsewhere in the literature. When using transfer learning methods, we
find that inheriting model weights pre-trained on FIRST images can boost model
performance when re-training on lower resolution NVSS data, but that inheriting
pre-trained model weights from NVSS and re-training on FIRST data impairs the
performance of the classifier. We consider the implication of these results in
the context of future radio surveys planned for next-generation radio
telescopes such as ASKAP, MeerKAT, and SKA1-MID
A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
We present a new non-parametric method to quantify morphologies of galaxies
based on a particular family of learning machines called support vector
machines. The method, that can be seen as a generalization of the classical CAS
classification but with an unlimited number of dimensions and non-linear
boundaries between decision regions, is fully automated and thus particularly
well adapted to large cosmological surveys. The source code is available for
download at http://www.lesia.obspm.fr/~huertas/galsvm.html To test the method,
we use a seeing limited near-infrared ( band, ) sample observed
with WIRCam at CFHT at a median redshift of . The machine is trained
with a simulated sample built from a local visually classified sample from the
SDSS chosen in the high-redshift sample's rest-frame (i band, ) and
artificially redshifted to match the observing conditions. We use a
12-dimensional volume, including 5 morphological parameters and other
caracteristics of galaxies such as luminosity and redshift. We show that a
qualitative separation in two main morphological types (late type and early
type) can be obtained with an error lower than 20% up to the completeness limit
of the sample () which is more than 2 times better that what would
be obtained with a classical C/A classification on the same sample and indeed
comparable to space data. The method is optimized to solve a specific problem,
offering an objective and automated estimate of errors that enables a
straightforward comparison with other surveys.Comment: 11 pages, 7 figures, 3 tables. Submitted to A&A. High resolution
images are available on reques
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