755 research outputs found
Ganalyzer: A tool for automatic galaxy image analysis
We describe Ganalyzer, a model-based tool that can automatically analyze and
classify galaxy images. Ganalyzer works by separating the galaxy pixels from
the background pixels, finding the center and radius of the galaxy, generating
the radial intensity plot, and then computing the slopes of the peaks detected
in the radial intensity plot to measure the spirality of the galaxy and
determine its morphological class. Unlike algorithms that are based on machine
learning, Ganalyzer is based on measuring the spirality of the galaxy, a task
that is difficult to perform manually, and in many cases can provide a more
accurate analysis compared to manual observation. Ganalyzer is simple to use,
and can be easily embedded into other image analysis applications. Another
advantage is its speed, which allows it to analyze ~10,000,000 galaxy images in
five days using a standard modern desktop computer. These capabilities can make
Ganalyzer a useful tool in analyzing large datasets of galaxy images collected
by autonomous sky surveys such as SDSS, LSST or DES. The software is available
for free download at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer, and
the data used in the experiment are available at
http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer/GalaxyImages.zip.Comment: ApJ, accepte
Automatic quantitative morphological analysis of interacting galaxies
The large number of galaxies imaged by digital sky surveys reinforces the
need for computational methods for analyzing galaxy morphology. While the
morphology of most galaxies can be associated with a stage on the Hubble
sequence, morphology of galaxy mergers is far more complex due to the
combination of two or more galaxies with different morphologies and the
interaction between them. Here we propose a computational method based on
unsupervised machine learning that can quantitatively analyze morphologies of
galaxy mergers and associate galaxies by their morphology. The method works by
first generating multiple synthetic galaxy models for each galaxy merger, and
then extracting a large set of numerical image content descriptors for each
galaxy model. These numbers are weighted using Fisher discriminant scores, and
then the similarities between the galaxy mergers are deduced using a variation
of Weighted Nearest Neighbor analysis such that the Fisher scores are used as
weights. The similarities between the galaxy mergers are visualized using
phylogenies to provide a graph that reflects the morphological similarities
between the different galaxy mergers, and thus quantitatively profile the
morphology of galaxy mergers.Comment: Astronomy & Computing, accepte
New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift
Testing theories of hierarchical structure formation requires estimating the
distribution of galaxy morphologies and its change with redshift. One aspect of
this investigation involves identifying galaxies with disturbed morphologies
(e.g., merging galaxies). This is often done by summarizing galaxy images
using, e.g., the CAS and Gini-M20 statistics of Conselice (2003) and Lotz et
al. (2004), respectively, and associating particular statistic values with
disturbance. We introduce three statistics that enhance detection of disturbed
morphologies at high-redshift (z ~ 2): the multi-mode (M), intensity (I), and
deviation (D) statistics. We show their effectiveness by training a
machine-learning classifier, random forest, using 1,639 galaxies observed in
the H band by the Hubble Space Telescope WFC3, galaxies that had been
previously classified by eye by the CANDELS collaboration (Grogin et al. 2011,
Koekemoer et al. 2011). We find that the MID statistics (and the A statistic of
Conselice 2003) are the most useful for identifying disturbed morphologies.
We also explore whether human annotators are useful for identifying disturbed
morphologies. We demonstrate that they show limited ability to detect
disturbance at high redshift, and that increasing their number beyond
approximately 10 does not provably yield better classification performance. We
propose a simulation-based model-fitting algorithm that mitigates these issues
by bypassing annotation.Comment: 15 pages, 14 figures, accepted for publication in MNRA
Automatic morphological classification of galaxy images
We describe an image analysis supervised learning algorithm that can
automatically classify galaxy images. The algorithm is first trained using a
manually classified images of elliptical, spiral, and edge-on galaxies. A large
set of image features is extracted from each image, and the most informative
features are selected using Fisher scores. Test images can then be classified
using a simple Weighted Nearest Neighbor rule such that the Fisher scores are
used as the feature weights. Experimental results show that galaxy images from
Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on
galaxies with accuracy of ~90% compared to classifications carried out by the
author. Full compilable source code of the algorithm is available for free
download, and its general-purpose nature makes it suitable for other uses that
involve automatic image analysis of celestial objects.Comment: Accepted for publication in MNRA
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