Parameterization of RVS synthetic stellar spectra for the ESA Gaia mission: Study of the optimal domain for ANN training

• Published in: Expert systems with applications 2010-03, Vol.37 (2), p.1719-1727
• Main Authors: Ordóñez, Diego, Dafonte, Carlos, Manteiga, Minia, Arcay, Bernardino
• Format: Article
• Language: English
• Subjects: Abundance; Artificial Neural Network; Connectionist systems; FFT; Gaia mission; Galaxies; Learning theory; Neural networks; Parametrization; Spectra; Stars; Stellar parameters; Stellar spectra; Wavelet transform
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2009.07.038

One of the upcoming cornerstone missions of the European Space Agency (ESA) is Gaia, a spacecraft that will be launched in 2011 and will carry out a stereoscopic census of our Galaxy and its environment by measuring with unprecedented exactitude the astrometry (distance and movements), the photometric distribution from ultraviolet to the infrared of its components, and, in the case of the brightest objects (mainly stars), the spectrum with intermediate resolution in the region of the infrared CaII triplet, with a spectrograph known as Radial Velocity Spectrometer (RVS). Stars are the basic constituents of our Galaxy, and they can be characterized if we can estimate their principal atmospheric parameters: effective temperature, gravity, metal content (general abundance of elements other than H and He), and their abundance of alpha elements (elements with Z > 22 , [α/Fe]), which provide information on the physical environment in which the star was born. This work presents our results for the parameterization of stellar spectra with simulated data (synthetic spectra) in the spectral region of the RVS and with the application of Artificial Intelligence Techniques based on ANNs. Our work has two main purposes: to determine the optimal domain for the ANNs performance, and to develop an adequate noise detection and filtering algorithm.