Quasi-Stellar Object Selection Algorithm Using Time Variability and Machine Learning: Selection of 1620 Quasi-Stellar Object Candidates From Macho Large Magellanic Cloud Database

Kim, Dae Won; Protopapas, Pavlos; Byun, Yong Ik; Alcock, Charles; Khardon, Roni; Trichas, M.

doi:10.1088/0004-637x/735/2/68

Cited by 87 publications

(126 citation statements)

References 84 publications

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“…Initially, using a method purely based on the QSO time variability (Kim et al 2011) and later with the additional information from observations at other wavelengths and the development of a model based on the diagnostics of known QSOs (Kim et al 2012). each wave band strongly supporting the QSO selection criteria developed in this study.…”

Section: Non-qso Coloursmentioning

confidence: 55%

“…More recently, 1 QSO was confirmed by Hony et al (2011) and 145 QSOs by Kozłowski et al (2012), the latter identified from OGLE-III light-curves. The entire MACHO database, with light-curves spanning a time range of ∼7.5 yr, was searched by Kim et al (2011Kim et al ( , 2012 who trained a support vector machine model with diagnostic features based on mid-IR colours, spectral energy distribution red-shifts and X-ray luminosity of previously known QSOs to identify 663 high confidence candidates, but none of them are at present spectroscopically confirmed. Finally, as part of a spectroscopic study on post asymptotic giant branch (AGB)…”

Section: Appendix A: Known Qsos Behind the Magellanic Systemmentioning

confidence: 99%

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The VMC Survey

Cioni¹,

Kamath²,

Rubele³

et al. 2012

A&A

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Context. The number and spatial distribution of confirmed quasi-stellar objects (QSOs) behind the Magellanic system is limited. This undermines their use as astrometric reference objects for different types of studies. Aims. We have searched for criteria to identify candidate QSOs using observations from the VISTA survey of the Magellanic Clouds system (VMC) that provides photometry in the Y JK s bands and 12 epochs in the K s band. Methods. The (Y − J) versus (J − K s ) diagram has been used to distinguish QSO candidates from Milky Way stars and stars of the Magellanic Clouds. Then, the slope of variation in the K s band has been used to identify a sample of high confidence candidates. These criteria were developed based on the properties of 117 known QSOs presently observed by the VMC survey. Results. VMC Y JK s magnitudes and K s light-curves of known QSOs behind the Magellanic system are presented. About 75% of them show a slope of variation in K s > 10 −4 mag/day and the shape of the light-curve is in general irregular and without any clear periodicity. The number of QSO candidates found in tiles including the south ecliptic pole and the 30 Doradus regions is 22 and 26, respectively, with a ∼20% contamination by young stellar objects, planetary nebulae, stars and normal galaxies. Conclusions. By extrapolating the number of QSO candidates to the entire VMC survey area we expect to find about 1200 QSOs behind the LMC, 400 behind the SMC, 200 behind the Bridge and 30 behind the Stream areas, but not all will be suitable for astrometry. Further, the K s band light-curves can help support investigations of the mechanism responsible for the variations.

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Section: Non-qso Coloursmentioning

confidence: 55%

Section: Appendix A: Known Qsos Behind the Magellanic Systemmentioning

confidence: 99%

The VMC Survey

Cioni¹,

Kamath²,

Rubele³

et al. 2012

A&A

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“…The variable objects have to be distinguished from two broad types of interlopers: nonvariable objects and objects with corrupted photometry. To quantify the performance of each index following Kim et al (2011b) and Graham et al (2014), we compute the completeness C and purity P:…”

Section: Comparison Techniquementioning

confidence: 99%

Comparative performance of selected variability detection techniques in photometric time series data

Sokolovsky

Gavras

Karampelas

et al. 2016

Mon. Not. R. Astron. Soc.

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Photometric measurements are prone to systematic errors presenting a challenge to lowamplitude variability detection. In search for a general-purpose variability detection technique able to recover a broad range of variability types including currently unknown ones, we test 18 statistical characteristics quantifying scatter and/or correlation between brightness measurements. We compare their performance in identifying variable objects in seven time series data sets obtained with telescopes ranging in size from a telephoto lens to 1 m-class and probing variability on time-scales from minutes to decades. The test data sets together include lightcurves of 127539 objects, among them 1251 variable stars of various types and represent a range of observing conditions often found in ground-based variability surveys. The real data are complemented by simulations. We propose a combination of two indices that together recover a broad range of variability types from photometric data characterized by a wide variety of sampling patterns, photometric accuracies and percentages of outlier measurements. The first index is the interquartile range (IQR) of magnitude measurements, sensitive to variability irrespective of a time-scale and resistant to outliers. It can be complemented by the ratio of the lightcurve variance to the mean square successive difference, 1/η, which is efficient in detecting variability on time-scales longer than the typical time interval between observations. Variable objects have larger 1/η and/or IQR values than non-variable objects of similar brightness. Another approach to variability detection is to combine many variability indices using principal component analysis. We present 124 previously unknown variable stars found in the test data.

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“…The fields cover almost the entire LMC bar (10 square degrees) to a limiting magnitude of V ≈ 22. The training set contains 6059 labeled light curves (Kim et al 2011). Table 1 shows the number of light curves per each of the available classes.…”

Section: Macho Data Setmentioning

confidence: 99%

Meta-Classification for Variable Stars

Pichara

Protopapas

León

2016

ApJ

Self Cite

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The need for the development of automatic tools to explore astronomical databases has been recognized since the inception of CCDs and modern computers. Astronomers already have developed solutions to tackle several science problems, such as automatic classification of stellar objects, outlier detection, and globular clusters identification, among others. New scientific problems emerge, and it is critical to be able to reuse the models learned before, without rebuilding everything from the beginning when the sciencientific problem changes. In this paper, we propose a new meta-model that automatically integrates existing classification models of variable stars. The proposed meta-model incorporates existing models that are trained in a different context, answering different questions and using different representations of data. A conventional mixture of expert algorithms in machine learning literature cannot be used since each expert (model) uses different inputs. We also consider the computational complexity of the model by using the most expensive models only when it is necessary. We test our model with EROS-2 and MACHO data sets, and we show that we solve most of the classification challenges only by training a meta-model to learn how to integrate the previous experts.

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Quasi-Stellar Object Selection Algorithm Using Time Variability and Machine Learning: Selection of 1620 Quasi-Stellar Object Candidates From Macho Large Magellanic Cloud Database

Cited by 87 publications

References 84 publications

The VMC Survey

The VMC Survey

Comparative performance of selected variability detection techniques in photometric time series data

Meta-Classification for Variable Stars

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