Dissecting stellar chemical abundance space with t-SNE. Anders, F., Chiappini, C., Santiago, B. X., Matijevič, G., Queiroz, A. B., & Steinmetz, M. ArXiv e-prints, 1803:arXiv:1803.09341, March, 2018.
Dissecting stellar chemical abundance space with t-SNE [link]Paper  abstract   bibtex   
In the era of industrial Galactic astronomy and multi-object spectroscopic stellar surveys, the sample sizes and the number of available stellar chemical abundances have reached dimensions in which it has become difficult to process all the available information in an effective manner. In this paper we demonstrate the use of a dimensionality-reduction technique (t-distributed stochastic neighbour embedding, t-SNE) for analysing the stellar abundance-space distribution. While the non-parametric non-linear behaviour of this technique makes it difficult to estimate the significance of found abundance-space substructure, we show that our results depend little on parameter choices and are robust to abundance errors. By reanalysing the high-resolution high-signal-to-noise solar-neighbourhood HARPS-GTO sample with t-SNE, we find clearer chemical separations of the high- and low-[\${\textbackslash}alpha\$/Fe] disc sequences, hints for multiple populations in the high-[\${\textbackslash}alpha\$/Fe] population, and indications that the chemical evolution of the high-[\${\textbackslash}alpha\$/Fe] metal-rich stars is connected with the super-metal-rich stars. We also identify a number of chemically peculiar stars, among them the intermediate-age, extremely [Ti/Fe]-rich star HD14452, and a high-confidence s-process-enhanced abundance-ratio pair (HD91345/HD126681) with very similar ages and \$v_X\$ and \$v_Y\$ velocities, which we suggest to have a common birth origin, possibly a merged dwarf galaxy. Our results demonstrate the potential of t-SNE and similar methods for chemical-tagging studies of large spectroscopic surveys.
@article{anders_dissecting_2018,
	title = {Dissecting stellar chemical abundance space with t-{SNE}},
	volume = {1803},
	url = {http://adsabs.harvard.edu/abs/2018arXiv180309341A},
	abstract = {In the era of industrial Galactic astronomy and multi-object spectroscopic stellar surveys, the sample sizes and the number of available stellar chemical abundances have reached dimensions in which it has become difficult to process all the available information in an effective manner. In this paper we demonstrate the use of a
dimensionality-reduction technique (t-distributed stochastic neighbour embedding, t-SNE) for analysing the stellar abundance-space
distribution. While the non-parametric non-linear behaviour of this technique makes it difficult to estimate the significance of found abundance-space substructure, we show that our results depend little on parameter choices and are robust to abundance errors. By reanalysing the high-resolution high-signal-to-noise solar-neighbourhood HARPS-GTO sample with t-SNE, we find clearer chemical separations of the high- and low-[\${\textbackslash}alpha\$/Fe] disc sequences, hints for multiple populations in the high-[\${\textbackslash}alpha\$/Fe] population, and indications that the chemical evolution of the high-[\${\textbackslash}alpha\$/Fe] metal-rich stars is connected with the super-metal-rich stars. We also identify a number of chemically peculiar stars, among them the intermediate-age, extremely [Ti/Fe]-rich star HD14452, and a high-confidence s-process-enhanced abundance-ratio pair (HD91345/HD126681) with very similar ages and \$v\_X\$ and \$v\_Y\$ velocities, which we suggest to have a common birth origin, possibly a merged dwarf galaxy. Our results demonstrate the potential of t-SNE and similar methods for chemical-tagging studies of large spectroscopic surveys.},
	urldate = {2018-03-29},
	journal = {ArXiv e-prints},
	author = {Anders, Friedrich and Chiappini, Cristina and Santiago, Basílio X. and Matijevič, Gal and Queiroz, Anna B. and Steinmetz, Matthias},
	month = mar,
	year = {2018},
	keywords = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics},
	pages = {arXiv:1803.09341},
}

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