J/A+A/494/739 Automatic classification of OGLE variables (Sarro+, 2009)
Automatic classification of OGLE variables.
Sarro L.M., Debosscher J., Lopez M., Aerts C.
<Astron. Astrophys. 494, 739 (2009)>
=2009A&A...494..739S 2009A&A...494..739S
ADC_Keywords: Stars, variable ; MK spectral classification
Keywords: stars: variables: general - stars: binaries: general -
techniques: photometric - methods: data analysis -
methods: statistical
Abstract:
Scientific exploitation of large variability databases can only be
fully optimized if these archives contain, besides the actual
observations, annotations about the variability class of the objects
they contain. Supervised classification of observations produces these
tags, and makes it possible to generate refined candidate lists and
catalogues suitable for further investigation.
We aim to extend and test the classifiers presented in a previous work
against an independent dataset. We complement the assessment of the
validity of the classifiers by applying them to the set of OGLE light
curves treated as variable objects of unknown class. The results are
compared to published classification results based on the so-called
extractor methods.
Two complementary analyses are carried out in parallel. In both cases,
the original time series of OGLE observations of the Galactic bulge
and Magellanic Clouds are processed in order to identify and
characterize the frequency components. In the first approach, the
classifiers are applied to the data and the results analyzed in terms
of systematic errors and differences between the definition samples in
the training set and in the extractor rules. In the second approach,
the original classifiers are extended with colour information and,
again, applied to OGLE light curves.
We have constructed a classification system that can process huge
amounts of time series in negligible time and provide reliable samples
of the main variability classes. We have evaluated its strengths and
weaknesses and provide potential users of the classifier with a
detailed description of its characteristics to aid in the
interpretation of classification results. Finally, we apply the
classifiers to obtain object samples of classes not previously studied
in the OGLE database and analyse the results. We pay specific
attention to the B-stars in the samples, as their pulsations are
strongly dependent on metallicity.
Description:
Classification probabilities and class assignments are presented for
the OGLE Variability database, both on the basis of light curve
parameters alone, and in combination with Johnson photometry, for the
bulge data and Large and Small Magellanic Clouds.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
list.dat 173 12 List of files
gm/* . 6 *Classification of the OGLE bulge data using
the Gaussian Mixtures classifier
msbn/* . 6 *Classification of the OGLE bulge data using
the Multistage Bayesian Networks classifier
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Note on gm/* : files are named GM-AAA-BB.dat, and represent Classification
of the OGLE bulge (bul), LMC (lmc) or SMC (smc) data using light curve
attributes and (nc) the Gaussian Mixtures classifier or (vi) the V-I
colour index, and the Gaussian Mixtures classifier.
Note on msbn/* : files are named MSBN-AAA-BB.dat, and represent
Classification of the OGLE bulge (bul), LMC (lmc) or SMC (smc) data
using light curve attributes and (nc) the Multistage Bayesian Networks
classifier or (c) the V-I colour index, and the Multistage Bayesian
Networks classifier for LMC, the B-V and V-I colour indices, and the
Multistage Bayesian Networks classifier for Bulge and SMC.
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See also:
ftp://astro.princeton.edu/ogle : OGLE HomePage
Byte-by-byte Description of file: list.dat
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Bytes Format Units Label Explanations
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1- 20 A20 --- FileName File name
22- 27 I6 --- Nst Number of stars
29-173 A145 --- Title Title of the file
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Byte-by-byte Description of file: gm/*
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Bytes Format Units Label Explanations
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1- 28 A28 --- Name OGLE identifier
32- 36 A5 --- Class1 Most probable class
39- 43 A5 --- Class2 Second most probable class
46- 50 A5 --- Class3 Third most probable class
54- 60 F7.2 --- Mahala Mahalanobis distance to center of class
62- 69 E8.5 --- Prob1 Probability for class 1
71- 78 E8.5 --- Prob2 Probability for class 2
80- 87 E8.5 --- Prob3 Probability for class 3
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Byte-by-byte Description of file: msbn/*
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Bytes Format Units Label Explanations
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1- 28 A28 --- Name OGLE identifier
30- 34 A5 --- Class1 Most probable class
36- 40 A5 --- Class2 Second most probable class
42- 46 A5 --- Class3 Third most probable class
48- 55 E8.5 --- Prob1 Probability for class 1
58- 68 E11.5 --- Prob2 Probability for class 2
70- 80 E11.5 --- Prob3 Probability for class 3
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Acknowledgements:
Alexander Kopylov,
(End) Alexander Kopylov [SAO, Russia], Patricia Vannier [CDS] 26-Dec-2008