Astronomy and Astrophysics – Astronomy
Scientific paper
Oct 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008asvo.proc..129n&link_type=abstract
Astronomical Spectroscopy and Virtual Observatory, Proceedings of the EURO-VO Workshop, held at the European Space Astronomy Cen
Astronomy and Astrophysics
Astronomy
Scientific paper
We present the results of a novel application of Bayesian modelling techniques, which, although purely data driven, have a physically interpretable result, and will be useful as an efficient data mining tool. We base our pilot study on the UV-to-optical spectra (observed and synthetic) of early-type galaxies, with known star-formation histories. A probabilistic latent variable architecture is formulated, and a rigorous Bayesian methodology is employed for solving the inverse modelling problem from the available data. A powerful aspect of our formalism is that it allows us to recover a limited fraction of missing data due to incomplete spectral coverage, as well as to handle observational errors in a principled way. We find that our data-driven Bayesian modelling allows us to identify those early-types which contain a significant stellar population ≲ 1 Gyr old. We then apply our technique to the optical early-type galaxy spectra in the Sloan Digital Sky Survey. With this substantially larger data set (~26,000 galaxies), our method is sufficiently sensitive to identify those early-types which have undergone significant star formation in the last 4 Gyr, which allows us to explore how star formation is triggered and extinguished in early-type galaxies. This method can be extended to other data sets, and is therefore a very useful tool for automatically discovering various interesting sub-classes of galaxies, via rapid analysis of their spectra.
Harva Markus
Kaban Ata
Nolan Louisa
Raychaudhury Somak
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