Automated clustering algorithms for classification of astronomical objects

Zhang, Yanxia; Zhao, Yi

doi:10.1051/0004-6361:20040141

Cited by 52 publications

(39 citation statements)

References 39 publications

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“…The advantage that the SVM algorithm has over other algorithms is the ability to use all available information simultaneously, which has proven to be of great use in astronomy (e.g., Woźniak et al 2004;Zhang & Zhao 2004;or Huertas-Company et al 2008;Solarz et al 2012;Małek et al 2013). Here, we outline the basic idea behind the method, however, for a more in-depth description see Hsu et al (2003) or Cristianini & Shawe-Taylor (2000).…”

Section: Methodsmentioning

confidence: 99%

Clustering of the AKARI NEP deep field 24μm selected galaxies

Solarz

Pollo

Takeuchi

et al. 2015

A&A

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Aims. We present a method of selection of 24 μm galaxies from the AKARI north ecliptic pole (NEP) deep field down to 150 μJy and measurements of their two-point correlation function. We aim to associate various 24 μm selected galaxy populations with present day galaxies and to investigate the impact of their environment on the direction of their subsequent evolution. Methods. We discuss using of Support Vector Machines (SVM) algorithm applied to infrared photometric data to perform star-galaxy separation, in which we achieve an accuracy higher than 80%. The photometric redshift information, obtained through the CIGALE code, is used to explore the redshift dependence of the correlation function parameter (r 0 ) as well as the linear bias evolution. This parameter relates galaxy distribution to the one of the underlying dark matter. We connect the investigated sources to their potential local descendants through a simplified model of the clustering evolution without interactions. Results. We observe two different populations of star-forming galaxies, at z med ∼ 0.25, z med ∼ 0.9. Measurements of total infrared luminosities (L TIR ) show that the sample at z med ∼ 0.25 is composed mostly of local star-forming galaxies, while the sample at z med ∼ 0.9 is composed of luminous infrared galaxies (LIRGs) with L TIR ∼ 10 11.62 L . We find that dark halo mass is not necessarily correlated with the L TIR : for subsamples with L TIR = 10 11.15 L at z med ∼ 0.7 we observe a higher clustering length (r 0 = 6.21 ± 0.78 [h −1 Mpc]) than for a subsample with mean L TIR = 10 11.84 L at z med ∼ 1.1 (r 0 = 5.86 ± 0.69 h −1 Mpc). We find that galaxies at z med ∼ 0.9 can be ancestors of present day L * early type galaxies, which exhibit a very high r 0 ∼ 8 h −1 Mpc.

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Section: Methodsmentioning

confidence: 99%

Clustering of the AKARI NEP deep field 24μm selected galaxies

Solarz

Pollo

Takeuchi

et al. 2015

A&A

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“…We use the same sample as those adopted by Zhang and Zhao [18] . The X-ray data are adopted from the ROSAT All-Sky survey (RASS), which includes two parts: the RASS Bright Source Catalogue (RBSC) and the RASS Faint Source Catalogue (RFSC).…”

Section: Chosen Samplementioning

confidence: 99%

k-Nearest Neighbors for automated classification of celestial objects

Zhang

Zhao

2008

Sci. China Ser. G-Phys. Mech. Astron.

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The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric method of pattern recognition. In this paper, k-Nearest Neighbors, one of the most commonly used machine learning methods, work in automatic classification of multi-wavelength astronomical objects. Through the experiment, we conclude that the running speed of the kNN classier is rather fast and the classification accuracy is up to 97.73%. As a result, it is efficient and applicable to discriminate active objects from stars and normal galaxies with this method. The classifiers trained by the kNN method can be used to solve the automated classification problem faced by astronomy and the virtual observatory (VO).

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“…In astronomy, SVMs have been applied for identifying red variables (Williams, Wozniak, Vestrand, & Gupta, 2004), clustering astronomical objects (Zhang & Zhao, 2004), and classifying AGNs from stars and normal galaxies (Zhang, Cui, & Zhao, 2002).…”

Section: Support Vector Machinesmentioning

confidence: 99%