Corpus ID: 209439414

SSSpaNG! Stellar Spectra as Sparse, data-driven, Non-Gaussian processes.

@inproceedings{Feeney2019SSSpaNGSS,
  title={SSSpaNG! Stellar Spectra as Sparse, data-driven, Non-Gaussian processes.},
  author={Stephen M. Feeney and Benjamin D. Wandelt and Melissa K. Ness},
  year={2019}
}
Upcoming million-star spectroscopic surveys have the potential to revolutionize our view of the formation and chemical evolution of the Milky Way. Realizing this potential requires automated approaches to optimize estimates of stellar properties, such as chemical element abundances, from the spectra. The volume and quality of the observations strongly motivate that these approaches should be data-driven. With this in mind, we introduce SSSpaNG: a data-driven Gaussian Process model of stellar… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-3 OF 3 REFERENCES

2016, in Ground-based and Air

  • R SdeJong
  • 2016
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Stellar Spectra as Sparse Non-Gaussian Processes

  • P. Bonifacio, al. et, C. in Reylé, J. Richard, L. Cambrésy
  • 2016
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Gaussian processes for machine learning