Machine learning search for variable stars

Pashchenko, I. N.; Sokolovsky, K. V.; Gavras, P.

doi:10.1093/mnras/stx3222

Cited by 54 publications

(52 citation statements)

References 110 publications

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“…Objects for which the classifications differ will be inspected and added to the training sample with it's correct label. After we have created a sizable training sample, we will experiment with more sophisticated classifiers like stochastic gradient boosting (Chen & Guestrin 2016) and neural networks (e.g., LeCun et al 2015) which have been shown to perform better than random forest (Pashchenko et al 2018).…”

Section: Classification Of Periodic Variablesmentioning

confidence: 99%

Machine Learning for the Zwicky Transient Facility

Mahabal

Rebbapragada

Walters

et al. 2019

PASP

116

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The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids.We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.

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Section: Classification Of Periodic Variablesmentioning

confidence: 99%

Machine Learning for the Zwicky Transient Facility

Mahabal

Rebbapragada

Walters

et al. 2019

PASP

116

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“…Mitchell 1997;Lochner et al 2016), Neural Networks (NN Venables & Ripley 2002) and Random Forests(RF, e.g. Breiman 2001;du Buisson et al 2015;Pashchenko et al 2018).…”

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confidence: 99%

Identification of Young Stellar Object candidates in the Gaia DR2 x AllWISE catalogue with machine learning methods

Márton

Ábrahám

Szegedi-Elek

et al. 2019

Monthly Notices of the Royal Astronomical Society

158

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The second Gaia Data Release (DR2) contains astrometric and photometric data for more than 1.6 billion objects with mean Gaia G magnitude <20.7, including many Young Stellar Objects (YSOs) in different evolutionary stages. In order to explore the YSO population of the Milky Way, we combined the Gaia DR2 database with WISE and Planck measurements and made an all-sky probabilistic catalogue of YSOs using machine learning techniques, such as Support Vector Machines, Random Forests, or Neural Networks. Our input catalogue contains 103 million objects from the DR2xAllWISE cross-match table. We classified each object into four main classes: YSOs, extragalactic objects, main-sequence stars and evolved stars. At a 90% probability threshold we identified 1 129 295 YSO candidates. To demonstrate the quality and potential of our YSO catalogue, here we present two applications of it. (1) We explore the 3D structure of the Orion A star forming complex and show that the spatial distribution of the YSOs classified by our procedure is in agreement with recent results from the literature. (2) We use our catalogue to classify published Gaia Science Alerts. As Gaia measures the sources at multiple epochs, it can efficiently discover transient events, including sudden brightness changes of YSOs caused by dynamic processes of their circumstellar disk. However, in many cases the physical nature of the published alert sources are not known. A cross-check with our new catalogue shows that about 30% more of the published Gaia alerts can most likely be attributed to YSO activity. The catalogue can be also useful to identify YSOs among future Gaia alerts.

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“…The machine learning has been adopted as a successful alternative approach to defining reliable objects classes, stellar types and types of variable stars (eg. Liu et al 2015;Kovács & Szapudi 2015;Krakowski et al 2016;Kuntzer et al 2016;Sarro et al 2018;Pashchenko et al 2018). It is not the first time to take advantage of this technology to classify the objects or to regress the stellar parameters.…”

Section: Discussionmentioning

confidence: 99%

“…It finds natural patterns in data that generate insight and help us make better decisions and predictions 3 . Machine-learning algorithms have helped us to deal with complex problems in astrophysics, e.g., automatic galaxy classification (Huertas-Company et al 2008, the Morgan-Keenan spechttps://www.mathworks.com/solutions/machinelearning.html tral classification (MK; Manteiga et al 2009;Navarro et al 2012;Yi et al 2014), variable star classification (Pashchenko et al 2018) and spectral feature recognition for QSOs (Parks et al 2018).…”

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confidence: 99%

Machine Learning Applied to Star–Galaxy–QSO Classification and Stellar Effective Temperature Regression

Bai

Liu

Wang

et al. 2018

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In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised machine-learning algorithm, random forests (RF), to the star/galaxy/QSO classification and the stellar effective temperature regression based on the combination of LAMOST and SDSS spectroscopic data. This combination enable us to obtain reliable predictions with one of the largest training sample ever used. The training samples are built with nine-color data set of about three million objects for the classification and seven-color data set of over one million stars for the regression. The performance of the classification and regression is examined with the validation and the blind tests on the objects in the RAVE, 6dFGS, UVQS and APOGEE surveys. We demonstrate that the RF is an effective algorithm with the classification accuracies higher than 99% for the stars and the galaxies, and higher than 94% for the QSOs. These accuracies are higher than the machine-learning results in the former studies. The total standard deviations of the regression are smaller than 200 K that is similar to those of some spectrum-based methods. The machine-learning algorithm with the broad-band photometry provides us a more efficient approach to deal with massive amounts of astrophysical data than traditional color-cuts and SED fit.

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Machine learning search for variable stars

Cited by 54 publications

References 110 publications

Machine Learning for the Zwicky Transient Facility

Machine Learning for the Zwicky Transient Facility

Identification of Young Stellar Object candidates in the Gaia DR2 x AllWISE catalogue with machine learning methods

Machine Learning Applied to Star–Galaxy–QSO Classification and Stellar Effective Temperature Regression

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