J/MNRAS/488/2263 Classification of elusive astronomical objects (Bhavana+, 2019)
A classifier to detect elusive astronomical objects through photometry.
Bhavana D., Vig S., Ghosh S.K., Gorthi R.K.S.S.
<Mon. Not. R. Astron. Soc., 488, 2263-2274 (2019)>
=2019MNRAS.488.2263B 2019MNRAS.488.2263B (SIMBAD/NED BibCode)
ADC_Keywords: Stars, brown dwarf ; Photometry, infrared ; Positional data ;
Spectral types ; Models
Keywords: methods: statistical - techniques: miscellaneous -
techniques: photometric, brown dwarfs - infrared: stars
Abstract:
The application of machine learning principles in the photometric
search of elusive astronomical objects has been a less-explored
frontier of research. Here, we have used three methods, the neural
network and two variants of k-nearest neighbour, to identify brown
dwarf candidates using the photometric colours of known brown dwarfs.
We initially check the efficiencies of these three classification
techniques, both individually and collectively, on known objects. This
is followed by their application to three regions in the sky, namely
Hercules (2°x2°), Serpens (9°x4°), and Lyra
(2°x2°). Testing these algorithms on sets of objects that
include known brown dwarfs show a high level of completeness. This
includes the Hercules and Serpens regions where brown dwarfs have been
detected. We use these methods to search and identify brown dwarf
candidates towards the Lyra region. We infer that the collective
method of classification, also known as ensemble classifier, is highly
efficient in the identification of brown dwarf candidates.
Description:
NeuN and k-NN methods (k-NN-C and k-NN-TD) have been used for
classifying astronomical objects based on their photometric colours.
Although the methods are general and can be applied to select any
specific kind of astronomical objects, we have applied it to the
specific case of brown dwarfs. We use six colours from WISE (Wright et
al. 2010AJ....140.1868W 2010AJ....140.1868W) and 2MASS (Skrutskie et al.
2006AJ....131.1163S 2006AJ....131.1163S) as input features for the classification. We also
propose an ensemble classifier that identifies brown dwarf candidates
on the basis of a majority vote from the above three methods.
A number of training sets have been constructed for testing the
performance of the classifiers. This includes the 2-class and 3-class
training sets. In addition to the different techniques, we create
different training sets by combining templates from various known
brown dwarf and background object catalogues. In the 2-class
classification, both NeuN and the ensemble classifier emerge as the
best methods. Both NeuN and k-NN-C perform equally well in the 3-class
classification methods.
We apply the methods and optimal training sets to three regions in the
sky: Serpens, Hercules, and Lyra. Of these, Serpens and Hercules have
known brown dwarfs, previously identified by WISE.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table8.dat 117 9 Brown dwarf candidates identified by NeuN and
ensemble classifier in Hercules
table9.dat 120 15 Brown dwarf candidates identified by NeuN and
ensemble classifier in Lyra
table10.dat 120 64 Brown dwarf candidates identified by NeuN and
ensemble classifier in Serpens
table11.dat 82 21 The Gaia associations and their properties of
the brown dwarf candidates identified by NeuN
and ensemble classifier in all three regions
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See also:
VII/233 : The 2MASS Extended sources (IPAC/UMass, 2003-2006)
II/311 : WISE All-Sky Data Release (Cutri+ 2012)
Byte-by-byte Description of file: table8.dat table9.dat table10.dat
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Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 19 A19 --- Name WISE source name (JHHMMSS.ss+DDMMSS.s)
21- 28 F8.4 deg RAdeg Right ascension (J2000)
30- 36 F7.4 deg DEdeg Declination (J2000)
38- 65 A28 --- Tech Technique used to identify the brown dwarf
candidate (1)
67- 90 A24 --- SName Simbad association when found
92-120 A29 --- Type Star category
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Note (1): Brown dwarf candidates identified by NeuN and/or Ensemble classifiers.
The letters in parenthesis indicate the training set.
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Byte-by-byte Description of file: table11.dat
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Bytes Format Units Label Explanations
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1- 8 A8 --- Region Sky region (Hercules, Lyra, Serpens)
10- 17 F8.4 deg RAdeg Right ascension (J2000)
19- 25 F7.4 deg DEdeg Declination (J2000)
27- 45 I19 --- GaiaDR2 Gaia DR2 source identifier
47- 53 F7.4 mas plx Parallax
55- 64 F10.4 pc Dist Distance
66 A1 --- f_SpType [>] Flag on SpType (1)
67- 71 A5 --- SpType Spectral type
73- 82 F10.7 mas/yr PM Proper motion
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Note (1): Flag as follows:
> = indicates earlier spectral type
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History:
From electronic version of the journal
(End) Ana Fiallos [CDS] 08-Dec-2022