Machine learning in APOGEE. Unsupervised spectral classification with K-means

Garcia-Dias, R.; Allende Prieto, C.; Sánchez Almeida, J.; Ordovás-Pascual, I.
Bibliographical reference

Astronomy & Astrophysics, Volume 612, id.A98, 56 pp.

Advertised on:
Context. The volume of data generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive human interaction or are computationally expensive. In this scenario, machine learning, and unsupervised clustering algorithms in particular, offer interesting alternatives. The Apache Point Observatory Galactic Evolution Experiment (APOGEE) offers a vast data set of near-infrared stellar spectra, which is perfect for testing such alternatives. Aims: Our research applies an unsupervised classification scheme based on K-means to the massive APOGEE data set. We explore whether the data are amenable to classification into discrete classes. Methods: We apply the K-means algorithm to 153 847 high resolution spectra (R ≈ 22 500). We discuss the main virtues and weaknesses of the algorithm, as well as our choice of parameters. Results: We show that a classification based on normalised spectra captures the variations in stellar atmospheric parameters, chemical abundances, and rotational velocity, among other factors. The algorithm is able to separate the bulge and halo populations, and distinguish dwarfs, sub-giants, RC, and RGB stars. However, a discrete classification in flux space does not result in a neat organisation in the parameters' space. Furthermore, the lack of obvious groups in flux space causes the results to be fairly sensitive to the initialisation, and disrupts the efficiency of commonly-used methods to select the optimal number of clusters. Our classification is publicly available, including extensive online material associated with the APOGEE Data Release 12 (DR12). Conclusions: Our description of the APOGEE database can help greatly with the identification of specific types of targets for various applications. We find a lack of obvious groups in flux space, and identify limitations of the K-means algorithm in dealing with this kind of data. Full Tables B.1-B.4 are only available at the CDS via anonymous ftp to ( or via
Related projects
Project Image
Starbursts in Galaxies GEFE

Starsbursts play a key role in the cosmic evolution of galaxies, and thus in the star formation (SF) history of the universe, the production of metals, and the feedback coupling galaxies with the cosmic web. Extreme SF conditions prevail early on during the formation of the first stars and galaxies, therefore, the starburst phenomenon constitutes a

Muñoz Tuñón
spectrum of mercury lamp
Chemical Abundances in Stars

Stellar spectroscopy allows us to determine the properties and chemical compositions of stars. From this information for stars of different ages in the Milky Way, it is possible to reconstruct the chemical evolution of the Galaxy, as well as the origin of the elements heavier than boron, created mainly in stellar interiors. It is also possible to

Allende Prieto