J/A+A/612/A98APOGEE full information on classes (Garcia-Dias+, 2018)

Machine learning in APOGEE: Unsupervised spectral classification with K-means. Garcia-Dias R., Allende Prieto C., Sanchez Almeida J., Ordovas-Pascual I. <Astron. Astrophys. 612, A98 (2018)> =2018A&A...612A..98G (SIMBAD/NED BibCode)ADC_Keywords: Spectra, infrared ; MK spectral classification ; Stellar distributionKeywords: methods: data analysis - methods: numerical - catalogues - surveys - techniques: spectroscopic - Galaxy: stellar contentAbstract: 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. 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. 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. 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). 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.Description: Data for the classes derived on the paper. The tables provide the star labels, the mean spectra of the classes and the within class standard deviation.File Summary:

FileName Lrecl Records Explanations

ReadMe 80 . This file tableb2.dat 21 153847 Star labels tableb3.dat 505 8575 Mean spectra of the 50 classes tableb4.dat 505 8575 Within class standard deviation

Byte-by-byte Description of file: tableb2.dat

Bytes Format Units Label Explanations

1- 18 A18 --- APOGEE Star ID as defined in APOGEE DR12 20- 21 I2 --- Class ? K-means class

Byte-by-byte Description of file: tableb3.dat

Bytes Format Units Label Explanations

1- 8 F8.6 --- class00 Mean normalized flux of class 0 10- 17 F8.6 --- class01 Mean normalized flux of class 1 19- 26 F8.6 --- class02 Mean normalized flux of class 2 28- 35 F8.6 --- class03 Mean normalized flux of class 3 37- 44 F8.6 --- class04 Mean normalized flux of class 4 46- 53 F8.6 --- class05 Mean normalized flux of class 5 55- 62 F8.6 --- class06 Mean normalized flux of class 6 64- 71 F8.6 --- class07 Mean normalized flux of class 7 73- 80 F8.6 --- class08 Mean normalized flux of class 8 82- 89 F8.6 --- class09 Mean normalized flux of class 9 91- 98 F8.6 --- class10 Mean normalized flux of class 10 100-107 F8.6 --- class11 Mean normalized flux of class 11 109-116 F8.6 --- class12 Mean normalized flux of class 12 118-125 F8.6 --- class13 Mean normalized flux of class 13 127-134 F8.6 --- class14 Mean normalized flux of class 14 136-143 F8.6 --- class15 Mean normalized flux of class 15 145-152 F8.6 --- class16 Mean normalized flux of class 16 154-161 F8.6 --- class17 Mean normalized flux of class 17 163-170 F8.6 --- class18 Mean normalized flux of class 18 172-179 F8.6 --- class19 Mean normalized flux of class 19 181-188 F8.6 --- class20 Mean normalized flux of class 20 190-197 F8.6 --- class21 Mean normalized flux of class 21 199-206 F8.6 --- class22 Mean normalized flux of class 22 208-216 F9.6 --- class23 Mean normalized flux of class 23 218-225 F8.6 --- class24 Mean normalized flux of class 24 227-234 F8.6 --- class25 Mean normalized flux of class 25 236-243 F8.6 --- class26 Mean normalized flux of class 26 245-252 F8.6 --- class27 Mean normalized flux of class 27 254-261 F8.6 --- class28 Mean normalized flux of class 28 263-270 F8.6 --- class29 Mean normalized flux of class 29 272-280 F9.6 --- class30 Mean normalized flux of class 30 282-289 F8.6 --- class31 Mean normalized flux of class 31 291-300 F10.6 --- class32 Mean normalized flux of class 32 302-311 F10.6 --- class33 Mean normalized flux of class 33 313-320 F8.6 --- class34 Mean normalized flux of class 34 322-332 F11.6 --- class35 Mean normalized flux of class 35 334-341 F8.6 --- class36 Mean normalized flux of class 36 343-352 F10.6 --- class37 Mean normalized flux of class 37 354-362 F9.6 --- class38 Mean normalized flux of class 38 364-373 F10.6 --- class39 Mean normalized flux of class 39 375-385 F11.6 --- class40 Mean normalized flux of class 40 387-396 F10.6 --- class41 Mean normalized flux of class 41 398-405 F8.6 --- class42 Mean normalized flux of class 42 407-418 F12.6 --- class43 Mean normalized flux of class 43 420-430 F11.6 --- class44 Mean normalized flux of class 44 432-442 F11.6 --- class45 Mean normalized flux of class 45 444-455 F12.6 --- class46 Mean normalized flux of class 46 457-466 F10.6 --- class47 Mean normalized flux of class 47 468-479 F12.6 --- class48 Mean normalized flux of class 48 481-492 F12.6 --- class49 Mean normalized flux of class 49 494-503 F10.4 0.1nm Wavelength Wavelength in vacuum 505 I1 --- Mask [0/1] 1 if the pixel is used in k-means, 0 otherwise.

Byte-by-byte Description of file: tableb4.dat

Bytes Format Units Label Explanations

1- 8 F8.6 --- e_class00 Standard deviation of normalised flux in class 0 10- 17 F8.6 --- e_class01 Standard deviation of normalised flux in class 1 19- 26 F8.6 --- e_class02 Standard deviation of normalised flux in class 2 28- 35 F8.6 --- e_class03 Standard deviation of normalised flux in class 3 37- 44 F8.6 --- e_class04 Standard deviation of normalised flux in class 4 46- 53 F8.6 --- e_class05 Standard deviation of normalised flux in class 5 55- 62 F8.6 --- e_class06 Standard deviation of normalised flux in class 6 64- 71 F8.6 --- e_class07 Standard deviation of normalised flux in class 7 73- 80 F8.6 --- e_class08 Standard deviation of normalised flux in class 8 82- 89 F8.6 --- e_class09 Standard deviation of normalised flux in class 9 91- 98 F8.6 --- e_class10 Stanard deviation of normalised flux in class 10 100-107 F8.6 --- e_class11 Standard deviation of normalised flux in class 11 109-116 F8.6 --- e_class12 Standard deviation of normalised flux in class 12 118-125 F8.6 --- e_class13 Standard deviation of normalised flux in class 13 127-134 F8.6 --- e_class14 Standard deviation of normalised flux in class 14 136-143 F8.6 --- e_class15 Standard deviation of normalised flux in class 15 145-152 F8.6 --- e_class16 Standard deviation of normalised flux in class 16 154-161 F8.6 --- e_class17 Standard deviation of normalised flux in class 17 163-170 F8.6 --- e_class18 Standard deviation of normalised flux in class 18 172-179 F8.6 --- e_class19 Standard deviation of normalised flux in class 19 181-188 F8.6 --- e_class20 Standard deviation of normalised flux in class 20 190-197 F8.6 --- e_class21 Standard deviation of normalised flux in class 21 199-206 F8.6 --- e_class22 Standard deviation of normalised flux in class 22 208-216 F9.6 --- e_class23 Standard deviation of normalised flux in class 23 218-225 F8.6 --- e_class24 Standard deviation of normalised flux in class 24 227-234 F8.6 --- e_class25 Standard deviation of normalised flux in class 25 236-243 F8.6 --- e_class26 Standard deviation of normalised flux in class 26 245-252 F8.6 --- e_class27 Standard deviation of normalised flux in class 27 254-261 F8.6 --- e_class28 Standard deviation of normalised flux in class 28 263-270 F8.6 --- e_class29 Standard deviation of normalised flux in class 29 272-280 F9.6 --- e_class30 Standard deviation of normalised flux in class 30 282-289 F8.6 --- e_class31 Standard deviation of normalised flux in class 31 291-300 F10.6 --- e_class32 Standard deviation of normalised flux in class 32 302-311 F10.6 --- e_class33 Standard deviation of normalised flux in class 33 313-320 F8.6 --- e_class34 Standard deviation of normalised flux in class 34 322-332 F11.6 --- e_class35 Standard deviation of normalised flux in class 35 334-341 F8.6 --- e_class36 Standard deviation of normalised flux in class 36 343-352 F10.6 --- e_class37 Standard deviation of normalised flux in class 37 354-362 F9.6 --- e_class38 Standard deviation of normalised flux in class 38 364-373 F10.6 --- e_class39 Standard deviation of normalised flux in class 39 375-385 F11.6 --- e_class40 Standard deviation of normalised flux in class 40 387-396 F10.6 --- e_class41 Standard deviation of normalised flux in class 41 398-405 F8.6 --- e_class42 Standard deviation of normalised flux in class 42 407-418 F12.6 --- e_class43 Standard deviation of normalised flux in class 43 420-430 F11.6 --- e_class44 Standard deviation of normalised flux in class 44 432-442 F11.6 --- e_class45 Standard deviation of normalised flux in class 45 444-455 F12.6 --- e_class46 Standard deviation of normalised flux in class 46 457-466 F10.6 --- e_class47 Standard deviation of normalised flux in class 47 468-479 F12.6 --- e_class48 Standard deviation of normalised flux in class 48 481-492 F12.6 --- e_class49 Standard deviation of normalised flux in class 49 494-503 F10.4 0.1nm Wavelength Wavelength in vacuum 505 I1 --- Mask [0/1] 1 if the pixel is used in k-means, 0 otherwise.

Acknowledgements: Rafael Garcia-Dias, rafaelagd(at)gmail.com(End)Rafael Garcia-Dias Patricia Vannier [CDS] 07-Feb-2018

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