Classification and parametrization of unresolved galaxies with gaia

Tsalmantza, P; Kontizas, M; Rocca-Volmerange, B; Bailer-Jones, CAL; Kontizas, E; Bellas-Velidis, I; Korakitis, R; Livanou, E; Dapergolas, A; Vallenari, A; Fioc, M

dc.contributor.author	Tsalmantza, P	en
dc.contributor.author	Kontizas, M	en
dc.contributor.author	Rocca-Volmerange, B	en
dc.contributor.author	Bailer-Jones, CAL	en
dc.contributor.author	Kontizas, E	en
dc.contributor.author	Bellas-Velidis, I	en
dc.contributor.author	Korakitis, R	en
dc.contributor.author	Livanou, E	en
dc.contributor.author	Dapergolas, A	en
dc.contributor.author	Vallenari, A	en
dc.contributor.author	Fioc, M	en
dc.date.accessioned	2014-03-01T02:51:34Z
dc.date.available	2014-03-01T02:51:34Z
dc.date.issued	2008	en
dc.identifier.issn	0094243X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35561
dc.subject	Classification	en
dc.subject	Galaxies	en
dc.subject	Photometry	en
dc.subject	Spectroscopy	en
dc.title	Classification and parametrization of unresolved galaxies with gaia	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1063/1.3059019	en
heal.identifier.secondary	http://dx.doi.org/10.1063/1.3059019	en
heal.publicationDate	2008	en
heal.abstract	The ESA satellite mission Gaia will acquire spectrophotometric observations of several million unresolved galaxies during its five years of operation. Our objective is to design and implement a classification system for these data. For this purpose we need to build a new library of galaxy spectra which covers the necessary parameter space. Using the evolutionary code PÉGASE.2 we have produced a library of 28885 synthetic galaxy spectra at zero redshift covering four general spectral types of galaxies over the wavelength range from 250 to 1050 nm, at a sampling of 1nm or less. The library was also reproduced for 4 random values of redshift in the range of 0-0.2 and it is computed on a random grid of four key astrophysical parameters (3 for SFR and 1 for timescale of the infall of gas). The synthetic library was compared with various photometric and spectroscopic observations (e.g. from SDSS) and found in good agreement with them. Using simulated Gaia photometry of this library we train and test the performance of Support Vector Machine (SVM) classifiers and parametrizers. The first results are promising, indicating that galaxy types can be reliably predicted and several parameters (e.g. redshift, mass to light ratio, present SFR) can be estimated with low bias and variance from Gaia observations. © 2008 American Institute of Physics.	en
heal.journalName	AIP Conference Proceedings	en
dc.identifier.doi	10.1063/1.3059019	en
dc.identifier.volume	1082	en
dc.identifier.spage	111	en
dc.identifier.epage	118	en