35,307 research outputs found
A fast empirical method for galaxy shape measurements in weak lensing surveys
We describe a simple and fast method to correct ellipticity measurements of
galaxies from the distortion by the instrumental and atmospheric point spread
function (PSF), in view of weak lensing shear measurements. The method performs
a classification of galaxies and associated PSFs according to measured shape
parameters, and corrects the measured galaxy ellipticites by querying a large
lookup table (LUT), built by supervised learning. We have applied this new
method to the GREAT10 image analysis challenge, and present in this paper a
refined solution that obtains the competitive quality factor of Q = 104,
without any shear power spectrum denoising or training. Of particular interest
is the efficiency of the method, with a processing time below 3 ms per galaxy
on an ordinary CPU.Comment: 8 pages, 6 figures. Metric values updated according to the final
GREAT10 analysis software (Kitching et al. 2012, MNRAS 423, 3163-3208), no
qualitative changes. Associated code available at
http://lastro.epfl.ch/megalu
Identifying Galaxy Mergers in Observations and Simulations with Deep Learning
Mergers are an important aspect of galaxy formation and evolution. We aim to
test whether deep learning techniques can be used to reproduce visual
classification of observations, physical classification of simulations and
highlight any differences between these two classifications. With one of the
main difficulties of merger studies being the lack of a truth sample, we can
use our method to test biases in visually identified merger catalogues. A
convolutional neural network architecture was developed and trained in two
ways: one with observations from SDSS and one with simulated galaxies from
EAGLE, processed to mimic the SDSS observations. The SDSS images were also
classified by the simulation trained network and the EAGLE images classified by
the observation trained network. The observationally trained network achieves
an accuracy of 91.5% while the simulation trained network achieves 65.2% on the
visually classified SDSS and physically classified EAGLE images respectively.
Classifying the SDSS images with the simulation trained network was less
successful, only achieving an accuracy of 64.6%, while classifying the EAGLE
images with the observation network was very poor, achieving an accuracy of
only 53.0% with preferential assignment to the non-merger classification. This
suggests that most of the simulated mergers do not have conspicuous merger
features and visually identified merger catalogues from observations are
incomplete and biased towards certain merger types. The networks trained and
tested with the same data perform the best, with observations performing better
than simulations, a result of the observational sample being biased towards
conspicuous mergers. Classifying SDSS observations with the simulation trained
network has proven to work, providing tantalizing prospects for using
simulation trained networks for galaxy identification in large surveys.Comment: Submitted to A&A, revised after first referee report. 20 pages, 22
figures, 14 tables, 1 appendi
A PCA-based automated finder for galaxy-scale strong lenses
We present an algorithm using Principal Component Analysis (PCA) to subtract
galaxies from imaging data, and also two algorithms to find strong,
galaxy-scale gravitational lenses in the resulting residual image. The combined
method is optimized to find full or partial Einstein rings. Starting from a
pre-selection of potential massive galaxies, we first perform a PCA to build a
set of basis vectors. The galaxy images are reconstructed using the PCA basis
and subtracted from the data. We then filter the residual image with two
different methods. The first uses a curvelet (curved wavelets) filter of the
residual images to enhance any curved/ring feature. The resulting image is
transformed in polar coordinates, centered on the lens galaxy center. In these
coordinates, a ring is turned into a line, allowing us to detect very faint
rings by taking advantage of the integrated signal-to-noise in the ring (a line
in polar coordinates). The second way of analysing the PCA-subtracted images
identifies structures in the residual images and assesses whether they are
lensed images according to their orientation, multiplicity and elongation. We
apply the two methods to a sample of simulated Einstein rings, as they would be
observed with the ESA Euclid satellite in the VIS band. The polar coordinates
transform allows us to reach a completeness of 90% and a purity of 86%, as soon
as the signal-to-noise integrated in the ring is higher than 30, and almost
independent of the size of the Einstein ring. Finally, we show with real data
that our PCA-based galaxy subtraction scheme performs better than traditional
subtraction based on model fitting to the data. Our algorithm can be developed
and improved further using machine learning and dictionary learning methods,
which would extend the capabilities of the method to more complex and diverse
galaxy shapes
Machine-learning identification of galaxies in the WISExSuperCOSMOS all-sky catalogue
The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS,
were cross-matched by Bilicki et al. (2016) (B16) to construct a novel
photometric redshift catalogue on 70% of the sky. Galaxies were therein
separated from stars and quasars through colour cuts, which may leave
imperfections because of mixing different source types which overlap in colour
space. The aim of the present work is to identify galaxies in the
WISExSuperCOSMOS catalogue through an alternative approach of machine learning.
This allows us to define more complex separations in the multi-colour space
than possible with simple colour cuts, and should provide more reliable source
classification. For the automatised classification we use the support vector
machines learning algorithm, employing SDSS spectroscopic sources cross-matched
with WISExSuperCOSMOS as the training and verification set. We perform a number
of tests to examine the behaviour of the classifier (completeness, purity and
accuracy) as a function of source apparent magnitude and Galactic latitude. We
then apply the classifier to the full-sky data and analyse the resulting
catalogue of candidate galaxies. We also compare thus produced dataset with the
one presented in B16. The tests indicate very high accuracy, completeness and
purity (>95%) of the classifier at the bright end, deteriorating for the
faintest sources, but still retaining acceptable levels of 85%. No significant
variation of classification quality with Galactic latitude is observed.
Application of the classifier to all-sky WISExSuperCOSMOS data gives 15 million
galaxies after masking problematic areas. The resulting sample is purer than
the one in B16, at a price of lower completeness over the sky. The automatic
classification gives a successful alternative approach to defining a reliable
galaxy sample as compared to colour cuts.Comment: 12 pages, 15 figures, accepted for publication in A&A. Obtained
catalogue will be included in the public release of the WISExSuperCOSMOS
galaxy catalogue available from http://ssa.roe.ac.uk/WISExSCO
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