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J/ApJS/230/20 Machine learning technique to classify CoNFIG gal. (Aniyan+, 2017)

Classifying radio galaxies with the convolutional neural network. Aniyan A.K., Thorat K. <Astrophys. J. Suppl. Ser., 230, 20-20 (2017)> =2017ApJS..230...20A (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies, radio Keywords: methods: miscellaneous; methods: observational; radio continuum: galaxies; techniques: miscellaneous Abstract: We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)-Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a "fusion classifier," which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs. Description: We initially selected the FRI-II sample from a subset of the Combined NVSS and FIRST Galaxies sample (CoNFIG henceforth; Gendre & Wall 2008, J/MNRAS/390/819; Gendre+ 2010, J/MNRAS/404/1719). File Summary:
FileName Lrecl Records Explanations
ReadMe 80 . This file table5.dat 68 187 Table of predictions for validation samples
See also: VIII/65 : 1.4GHz NRAO VLA Sky Survey (NVSS) (Condon+ 1998) VIII/92 : The FIRST Survey Catalog, Version 2014Dec17 (Helfand+ 2015) J/MNRAS/390/819 : Combined NVSS-FIRST Galaxies (CoNFIG) sample (Gendre+, 2008) J/MNRAS/404/1719 : CoNFIG sample II (Gendre+, 2010) J/ApJS/194/31 : Morphology for groups in the FIRST database (Proctor, 2011) J/MNRAS/421/1569 : Properties of 18286 SDSS radio galaxies (Best+, 2012) J/MNRAS/430/3086 : CoNFIG AGN sample (Gendre+, 2013) J/MNRAS/446/2985 : Double-lobed radio sources catalog (van Velzen+, 2015) Byte-by-byte Description of file: table5.dat
Bytes Format Units Label Explanations
1- 19 A19 --- Name Source Name 21- 22 I2 h RAh Hour of Right Ascension (J2000) 24- 25 I2 min RAm Minute of Right Ascension (J2000) 27- 32 F6.3 s RAs Second of Right Ascension (J2000) 34 A1 --- DE- Sign of the Declination (J2000) 35- 36 I2 deg DEd Degree of Declination (J2000) 38- 39 I2 arcmin DEm Arcminute of Declination (J2000) 41- 45 F5.2 arcsec DEs Arcsecond of Declination (J2000) 47- 50 A4 --- True True Class of Source (1) 52- 58 A7 --- Pred Prediction by algorithm (1) 60- 68 F9.5 --- Prob Probability Score of Prediction (1)
Note (1): This table contains the prediction results for the validation samples from Config samples. The true class and the prediction by the fusion classifier is given along with the probability scores. BT = Bent-tailed (77 true; 64 predicted) FRI = Fanaroff-Riley (FR) I (53 true; 53 predicted) FRII = Fanaroff-Riley (FR) II (57 true; 69 predicted)
History: From electronic version of the journal
(End) Prepared by [AAS], Emmanuelle Perret [CDS] 07-Aug-2017
The document above follows the rules of the Standard Description for Astronomical Catalogues.From this documentation it is possible to generate f77 program to load files into arrays or line by line

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