J/MNRAS/477/894 SDSS galaxies classification (Abraham+, 2018)
Detection of bars in galaxies using a deep convolutional neural network.
Abraham S., Aniyan A.K., Kembhavi A.K., Philip N.S., Vaghmare K.
<Mon. Not. R. Astron. Soc., 477, 894-903 (2018)>
=2018MNRAS.477..894A 2018MNRAS.477..894A (SIMBAD/NED BibCode)
ADC_Keywords: Surveys ; Galaxies ; Morphology
Keywords: methods: data analysis - techniques: image processing - catalogues -
galaxies: general
Abstract:
We present an automated method for the detection of bar structure in
optical images of galaxies using a deep convolutional neural network
that is easy to use and provides good accuracy. In our study, we use a
sample of 9346 galaxies in the redshift range of 0.009-0.2 from the
Sloan Digital Sky Survey (SDSS), which has 3864 barred galaxies, the
rest being unbarred. We reach a top precision of 94 per cent in
identifying bars in galaxies using the trained network. This accuracy
matches the accuracy reached by human experts on the same data without
additional information about the images. Since deep convolutional
neural networks can be scaled to handle large volumes of data, the
method is expected to have great relevance in an era where astronomy
data is rapidly increasing in terms of volume, variety, volatility,
and velocity along with other V's that characterize big data. With the
trained model, we have constructed a catalogue of barred galaxies from
SDSS and made it available online.
Description:
In this work, we have used galaxy images from SDSS Data Release 13 to
train the network.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table4.dat 71 111838 Catalogue based on SDSS DR13
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See also:
http://www.sdss.org : SDSS Home Page
Byte-by-byte Description of file: table4.dat
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Bytes Format Units Label Explanations
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1- 19 I19 --- objID SDSS photometric object ID
21- 29 F9.5 deg RAdeg Rigth ascension (J2000)
31- 53 F23.19 deg DEdeg Declination (J2000)
55- 62 A8 --- Class Most probable class of the object
64- 71 F8.4 % Prob Prediction probability for the
most probable class
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History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 02-Jun-2021