Astrophysical Parameter Estimation for Gaia using Machine Learning Algorithms

Details Astrophysical Parameter Estimation for Gaia using Machine Learning Algorithms Astrophysical Parameter Estimation for Gaia using Machine Learning Algorithms

Aug 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008aspc..394..531t&link_type=abstract

Astronomical Data Analysis Software and Systems ASP Conference Series, Vol. 394, Proceedings of the conference held 23-26 Septem

Computer Science

Learning

Scientific paper

Gaia is the next astrometric mission from ESA and will measure objects up to a magnitude of about G=20. Depending on the kind of object (which will be determined automatically because Gaia does not hold an input catalogue), the specific astrophysical parameters will be estimated. The General Stellar Parametrizer (GSP-phot) estimates the astrophysical parameters based on low-dispersion spectra and parallax information for single stars. We show the results of machine learning algorithms trained on simulated data and further developments of the core algorithms which improve the accuracy of the estimated astrophysical parameters.

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