Data: 02.06.2016

Palestrante: Dr. Fabricio Ferrari (FURG)

Link: https://youtu.be/hv2DFp2N1fA

Resumo:

We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modified versions of the CASGM coefficients (Concentration $C1$ , Asymmetry $A_3$ , and Smoothness $S_3$ ), and the new parameters entropy, $H$, and spirality $sigmapsi$. The new parameters $A_3$ , $S_3$ , and $H$ are better to discriminate galaxy classes than $A_1$ , $S_1$ , and $G$, respectively. The new parameter $sigma_psi$ captures the amount of non-radial pattern on the image and is almost linearly dependent on T-type. Using a sample of spiral and elliptical galaxies from the Galaxy Zoo project as a training set, we employed the Linear Discriminant Analysis (LDA) technique to classify EFIGI (Baillard et al. 4458 galaxies), Nair & Abraham (14,123 galaxies), and SDSS Legacy (779,235 galaxies) samples. The cross-validation test shows that we can achieve an accuracy of more than 90% with our classification scheme. Therefore, we are able to define a plane in the morphometric parameter space that separates the elliptical and spiral classes with a mismatch between classes smaller than 10%. We use the distance to this plane as a morphometric index ($M_i$) and we show that it follows the human based T-type index very closely. We calculate morphometric index M i for ∼780k galaxies from SDSS Legacy Survey–DR7. We discuss how M i correlates with stellar population parameters obtained using the spectra available from SDSS–DR7. We discuss two science cases. We apply the morphometric system together with photometric analysis to discriminate classical from pseudo bulges in a sample of $sim$1000 SDSS galaxies from Gadotti (2009). Also, we compare morphometry and kinemtics for CALIFA survey galaxies.