ANNs and Wavelets: A Strategy for Gaia RVS Low S/N Stellar Spectra Parameterization

Details ANNs and Wavelets: A Strategy for Gaia RVS Low S/N Stellar Spectra Parameterization ANNs and Wavelets: A Strategy for Gaia RVS Low S/N Stellar Spectra Parameterization

May 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010pasp..122..608m&link_type=abstract

Publications of the Astronomical Society of the Pacific, Volume 122, issue 891, pp.608-617

Astronomy and Astrophysics

Astrophysics

1

Data Analysis And Techniques

Scientific paper

Gaia is the next astrometric ESA mission, conceived to extend the Hipparcos legacy by producing what has been called the first stereoscopic census of the Galaxy. The spacecraft will be launched by spring of 2012 and will measure astrometry with unprecedented accuracy for a significant 1% of the objects in the Milky Way. Additionally, two spectrophotometers will determine the spectral energy distributions (SEDs) of the objects in the region of 0.3-1 µm, and a radial velocity spectrograph (RVS) will measure the kinematics of the brightest objects (down to 17 mag). The Gaia RVS will provide spectra in the near-IR Ca II triplet region with an expected signal-to-noise ratio (S/N) between 100 and 20 for FGK stars with visual magnitude between 8 and 15. In order to deal with the enormous volume of data that the mission will generate, automated specialized analysis tools are being developed by the mission scientific Data Analysis and Processing Consortium (Gaia DPAC). In particular, we have been testing several analysis techniques in order to be prepared to extract all possible astrophysical information from RVS stellar spectra. A combination of data processing in transformed domains (Fourier analysis and wavelet multilevel decomposition) and connectionist systems (artificial neural networks, ANNs) has proven to be a good approach to derive the fundamental stellar parameters, T, log g, [Fe/H], and [α/Fe], on the basis of RVS synthetic spectra blurred with noise at different S/N. Signal-processing techniques allowed us to estimate and categorize the S/N, which in turn is found to be essential since the optimal algorithm for parameterization is highly dependent on S/N. In the case of low S/N (5-25) spectra, it is found that the wavelet transform provides a competitive approach for parameterization. The derivation of the stellar parameters is performed by the use of ANNs trained with the error backpropagation algorithm. The accuracy of the parameters' derivation is presented for typical Galaxy populations.

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