El software de medios: diez rasgos que lo caracterizan

In performing the galaxy modelling in the two different Sloan filters no fit- ting parameter is constrained to have the same value in both band-passes. This means that the thumbnail images in theiand r bands are fitted inde- pendently.

In Figure 3.16 and 3.17 the retrieved parameters in the Sloan i and r

bands are plotted against each others for the 1636 galaxies modelled in both bands by fitting a de Vaucouleurs profile to the bulge and an exponential to the disk. The same is shown in Figure 3.18 and 3.19 for the 1766 galax- ies modelled in both bands by fitting a S´ersic profile to the bulge and an exponential to the disk.

In the apparent magnitude plot there are only few isolated points while the others correlate well, the shift being due to the different zero points and the scatter in the color. The particular ”arrow shape” of the bulge–to–total light ratio can be easily explained since galaxies which result in pure bulge objects in one band can actually present a faint disk that can be detected in the other band, resulting in a smaller value for the bulge–to–disk ratio. There is a quite good agreement between the values of the scalelengths for the bulge and the disk component of galaxies measured in different filters,

3.5 Discussion 65

we interpret this result as a sign of the absence of color gradients in the structural subcomponents. Since a correlation is seen for the majority of the quantities,we conclude that the fit can be regarded as robust.

12 14 16 18 12 14 16 18 r 0 0.4 0.8 0 0.2 0.4 0.6 0.8 1 0 50 100 0 50 100 0 20 40 60 80 0 20 40 60 80

Figure 3.16: The total flux (upper–left), the bulge fraction (upper–right), the disk scalelength (lower–left) and the effective radius of the bulge (lower– right) in the i and r bands are plotted against each other for the 1636 galaxies modelled in both bands by fitting a de Vaucouleurs profile to the bulge and an exponential to the disk.

3.5 Discussion 67 0 0.4 0.8 0 0.2 0.4 0.6 0.8 1 0 30 60 90 0 30 60 90 0 30 60 90 0 30 60 90

Figure 3.17: The position angle of the disk (upper–left), the ellipticity of the bulge (lower–left) and the inclination angle of the disk (lower–right) in the i and r bands are plotted against each other for the 1636 galaxies modelled in both bands by fitting a de Vaucouleurs profile to the bulge and an exponential to the disk.

12 14 16 18 12 14 16 18 r 0 0.4 0.8 0 0.2 0.4 0.6 0.8 1 0 50 100 0 50 100 0 20 40 60 80 0 20 40 60 80

Figure 3.18: The same as Figure 3.16 but for the 1766 galaxies modelled in both bands by fitting a S´ersic profile to the bulge and an exponential to the disk.

3.5 Discussion 69 0 0.4 0.8 0 0.2 0.4 0.6 0.8 1 0 30 60 90 0 30 60 90 0 30 60 90 0 30 60 90

Figure 3.19: The same as Figure 3.17 but for the 1766 galaxies modelled in both bands by fitting a S´ersic profile to the bulge and an exponential to the disk.

Chapter 4

Morphological Classifiers

In this chapter we study the relations between quantitative morphological classifiers using our complete magnitude limited sample of 1862 galaxies drawn from the Sloan Digital Sky Survey. The sample includes bright ob- jects, r 6 15.9, in the nearby universe, z 6 0.12. It contains ellipticals, lenticulars, early and late–type spirals and irregulars. We consider struc- tural parameters, non-parametric and model–dependent ones, and photo- metric parameters which are suitable for quantitative, automatic and ob- jective galaxy classification. We first calibrate these parameters using the Hubble type, since “eye–ball” classification is provided for all the galaxies in the sample. We find that rest–frame galaxy colours, concentration index, bulge–to–disk ratio, effective surface brightness, mass–to–light ratio, residual and asymmetry parameters define a multi–parameter space in which galax- ies of all morphological types are located according to well defined physical properties. We conclude that the quantitative morphological classifiers we select allow us to build galaxy samples according to their stellar masses, mean stellar ages and gas–phase metallicities.

4.1

Introduction

Due to great improvement in observational techniques, digital sky surveys are nowadays performed routinely in various wavelengths, covering the en- tire electromagnetic spectrum, over extended areas on the sky. Photometric surveys are often supported by spectroscopic follow–up. Automatic pipelines process the data and spectro–photometric information is therefore available for an unprecedented number of objects. Age–old problems can be studied with the advantage of good statistics while new interesting research fields can be investigated, especially thanks to the advent of multi–object spec- troscopy.

In galaxy formation and evolution studies it is often required to deal with morphologically classified galaxy samples. The cosmic evolution of

galaxy morphology (Lilly et al. 1998; Marleau & Simard 1998), the study of the Fundamental Plane of spheroids (Djorgovski & Davis 1987) and of the Tully–Fisher relation of spiral galaxies (Tully & Fisher 1977) and the investigation of the morphological segregation of galaxies (Dressler 1980) are only a few examples of studies for which samples with precise morpho- logical properties are needed.

The morphological classification of galaxies aims to divide galaxies into types according to their shape. The appearance of galaxies is strongly de- pendent on projection effects: the disk of a galaxy is easily visible in edge–on systems while it could be undetectable for face–on systems if a spiral struc- ture is not present or the resolution is not high enough. Galaxy morphology also depends on the effect of dust extinction, which is smaller in the near– IR, and on the observing wavelength. The photometric band in which we observe becomes important when observing galaxies at different redshifts. The outcome of the classification will depend on the rest–frame wavelength sampled. Morphological k–correction can be quite significant and it plays an important role when we want to disentangle evolutionary effects from simple bandpass shifting.

Despite its subjective nature, visual classification has a long tradition. The most used galaxy classification scheme was introduced by Hubble (1939) and later on, after many modifications, brought to completion by Sandage (1961). Galaxies are placed in the Hubble scheme according to three main visual properties: the predominance of their bulge component; the degree to which spiral arms are resolved into stars and HII regions; and the tightness with which spiral arms are wound. When dealing with large datasets, such as the ones provided by large surveys, it is necessary to find quantitative measures which correlate with the Hubble scheme in order to perform auto- mated, reproducible and objective classifications. Many attempts have been made and classifications using colours, spectroscopic features or purely pho- tometric ones have been proposed. Strateva et al. (2001) show that galaxies have a bimodal u−r colour distribution, corresponding to early and late morphological types, which can be clearly separated by au−r = 2.22 colour cut. Spectroscopic features such as the 4000–˚A break strength (Dn4000) and

the Balmer absorption line Hδhave shown to be powerful probe of the recent star formation history of a galaxy (Kauffmann et al. 2003). In particular a value of Dn4000 = 1.8 can be used to separate galaxies dominated by an old

stellar population from galaxies with more recent star formation. A classi- fication scheme which combines spectral and structural parameters was in- troduced by Whitmore (1984) and revised by Bershady et al. (2000). Their multivariate photometric space can be used to reliably classify galaxies at different redshifts in a fully quantitative way.

It is worth mentioning that, although purely based on appearance, the so called “Hubble tuning fork” correlates well with physical galaxy character- istics such as: stellar masses, baryonic specific angular momentum, stellar