2018
DOI: 10.3847/1538-3881/aaf009
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Machine Learning Applied to Star–Galaxy–QSO Classification and Stellar Effective Temperature Regression

Abstract: 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 l… Show more

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Cited by 54 publications
(45 citation statements)
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“…In this second paper of the KiDS-SQuaD project we present a new ML-based classifier to identify extragalactic objects in a search for lensed quasars within the KiDS DR4 data. The technique adopted in this paper has become relatively standard in the extragalactic community to classify objects in multi-band photometric surveys (Gieseke et al 2011;Kovács & Szapudi 2015;Brescia et al 2015;Carrasco et al 2015;Peters et al 2015;Krakowski et al 2016Krakowski et al , 2018Viquar et al 2018;Barrientos et al 2018;Nolte et al 2019;Bai et al 2019;Nakoneczny et al 2019), which provide a very large amount of data.…”
Section: Results and Conclusionmentioning
confidence: 99%
See 1 more Smart Citation
“…In this second paper of the KiDS-SQuaD project we present a new ML-based classifier to identify extragalactic objects in a search for lensed quasars within the KiDS DR4 data. The technique adopted in this paper has become relatively standard in the extragalactic community to classify objects in multi-band photometric surveys (Gieseke et al 2011;Kovács & Szapudi 2015;Brescia et al 2015;Carrasco et al 2015;Peters et al 2015;Krakowski et al 2016Krakowski et al , 2018Viquar et al 2018;Barrientos et al 2018;Nolte et al 2019;Bai et al 2019;Nakoneczny et al 2019), which provide a very large amount of data.…”
Section: Results and Conclusionmentioning
confidence: 99%
“…Machine learning (ML) methods have proven to be very effective in identifying and classifying extragalactic sources (e.g. Eyer & Blake 2005;Elting et al 2008;Kim et al 2011;Gieseke et al 2011;Kovács & Szapudi 2015;Brescia et al 2015;Peters et al 2015;Krakowski et al 2016Krakowski et al , 2018Viquar et al 2018;Nolte et al 2019;Bai et al 2019) with respect to any manual colour cut. A specific type of classifiers, the ensembles of decision trees, were shown to be advantageous in the identification of extragalactic sources, and in particular quasars (Ball et al 2006;Carrasco et al 2015;Hernitschek et al 2016;Schindler et al 2017Schindler et al , 2018Sergeyev et al 2018;Jin et al 2019;Nakoneczny et al 2019).…”
Section: Introductionmentioning
confidence: 99%
“…Consequently, photometry data have been demonstrated to be useful as machine learning features in source-type classification by a number of studies (e.g. Carrasco et al 2015;Schindler et al 2019;Kang et al 2019;Nakoneczny et al 2019;Bai et al 2019). Furthermore, testing whether a source is resolved or unresolved can help distinguish the extended profiles of galaxies from stars and quasars (Aguado et al 2019;Baldry et al 2010;Morice-Atkinson et al 2018) and serve as a useful machine learning feature.…”
Section: Introductionmentioning
confidence: 99%
“…New technologies help us accelerate information acquisition from the huge datasets. Several studies have focused on developing classifiers, and they have proved that spectral-based methods are more reliable than those only based on photometric data (Bai et al 2018;Ball et al 2006).…”
Section: Datamentioning
confidence: 99%