2.6 Code testing
2.6.4 Structural parameters
The CAS parameters are model-independent measures of galaxy structure but, as in the case of the Sérsic index, there are several steps in their computation process that can be approached in more than a single way, making direct comparison with literature not straight forward. Similar to before, the general trends for galaxies of different types should however be comparable.
Concentration index
The concentration index can be defined terms of various growth curve radii: in this work, I usedr20andr80but other definitions have also been explored in literature. Furthermore, the values of the growth curve radii themselves depend on the description of the surface-brightness profiles of the galaxies. However, the dependence on the profiles should be weaker than that of the Sérsic index as C is a coarser measure thann, less sensitive to small-scale variations in the surface brightness. In Figure 2.6, I compare the values of C I measured for the galaxies in the CIG sample with the corresponding values obtained by HT08 and those found in the NSA. For the purpose of this test, in addition to the definition ofC employed in this work, I also use an alternative one, involvingr50andr90, in order to facilitate direct comparison with the NSA values. Again, the values of the Spearman rank correlation coefficients and the corre- spondingp-values are presented in the legend. The correlation between the values measured in this work and those found in literature is remarkably strong and statistically significant, with ρ(C80/20) = 0.944, ρ(C90/50) = 0.929 and p < 10−4 in both cases. As expected, the correlations are slightly tighter than those found for the Sérsic index.
2.6. Code testing
Figure 2.6: Comparison of the concentration indices,C80/20,C90/50, computed for the galaxies in the CIG sample with the corresponding literature values: HT08 and NSA, respectively. The legend shows the values of the Spearman rank correlation coefficient and the correspondingp-values. The dashed- dotted lines show the one-to-one relation.
Asymmetry and clumpiness
In the final test, I considered the values of the asymmetry and clumpiness parameters found for the galaxies in the CIG sample. One of the key factors that can potentially influence the values of bothAandSare the choice of the extraction region within which the measurement is performed. As described in Sections 2.4.3 and 2.4.5, in this study I used the radii derived from the detection masks (rma x) to define the extraction regions, instead of the commonly used 1.5×
rp (see e.g. Conselice 2003), as the aperture atrma x is more likely to enclose the low surface- brightness features in the outskirts of morphologically disturbed galaxies. This was previously demonstrated in Figure 2.1 and the effect is also apparent in Figure 2.10: the aperture atrma x
appears to be more suitable than the traditional definition in incorporating the outermost regions of some of the galaxies, mostly those viewed far from the face-on orientation.
Another factor that can affect the parameter values is the choice of the centroid. This is particularly important in the case of the asymmetry measure, where the difference of 1 pixel can result in the corresponding value ofAbeing altered by as much as 50% (Conselice et al., 2000). In this work, during the measurement of A, I used the minimum asymmetry centroid, following Conselice et al. (2000), however I took a slightly different approach to its computation (see section 2.2.2). In the case of the clumpiness parameter, the measurement will also depend on the choice of the smoothing kernel used; furthermore, the inner part of the galaxy should be disregarded during the measurement and the choice of that inner region
Figure 2.7: The dependance of the relation between the two measures of radii,r20and SDSS’(1.5× rp)/5, (used to define the inner cut-out apertures in the measurement of the clumpiness parameter (see
Section 2.4.5)) on the Sérsic index.
(including its size and position) will also affect the resulting values of S. As described in Section 2.4.5, I defined both the smoothing kernel and the inner region in terms of the growth curve radius, r20rather than the traditionally used (1.5×rp)/5 and I centred the inner region
on the highest-intensity pixel,OI ma x. In Figure 2.10, the definitions of the different radii can
be compared for all galaxies in the CIG sample: the grey apertures correspond to r20, the purple apertures to(1.5×rp)/5. In some cases the radii are only marginally different, but in
others they differ significantly. Within the test sample it appears that the discrepancies tend to be more pronounced in galaxies with high central concentration of light and this can be seen when plotting the ratio of the two radii against the Sérsic index (Figure 2.7). For galaxies with
n∼1 the ratio is close to 1 and it increases for those with highern, reaching values as high as 3-4. Upon visual inspection of the galaxy images, I decided to use r20as it is directly related to the size of the brightest inner regions in galaxies and is therefore less likely to overestimate the the size of the inner cut-out aperture. For consistency, the same radius was also chosen to define the size of the smoothing kernel.
Finally, the commonly used definitions of both Aand S include noise correction terms (Equations 2.8 and 2.9), which account for the asymmetry/clumpiness in the background at the galaxy position. In practice, this involves choosing a representative region of empty sky near the galaxy and repeating the measurement of eitherAandS within the empty aperture. Again, this could introduce discrepancies between the values of Aand S found in different studies. Clearly, both measures are strongly dependent on the choice of their computation
2.6. Code testing
method; therefore, in this test I focused on the bulk rather than individual values ofAandS
and their general behaviour when measured using different definitions.
In Figure 2.8 the values ofAandSmeasured in this work for the galaxies in the CIG sample are compared with previous studies, including the NSA and HT08. In the study by HT08, both parameters were calculated within 1.5×rpand the centres of the galaxies were considered to be the barycentres of their light distributions. The authors do not make further specifications regarding their methodology other than referring to the parameter definitions in Conselice (2003). It is therefore reasonable to assume that the parameters were calculated using the traditional approach described above, and consequently, to expect the values to deviate from those obtained in this work. I was unable to find information on the precise definitions ofA
andS used to obtain the values found in NSA.
Figure 2.8:Ther-band values of the asymmetry and clumpiness parameters, computed for the galaxies
in the CIG sample, compared with literature (HT08 and NSA). Left: asymmetry values found in this work (yellow) with and without the sky background correction (closed/open symbols, respectively) compared with HT08 (black) and NSA. Right: clumpiness values found in this work (yellow) and the corresponding reduced values obtained by cutting out the inner aperture atr20(red), in each case with and without the sky background correction (closed/open symbols, respectively), compared with HT08 (black) and NSA. The corresponding Spearman rank correlation coefficients (ρ) and the p-values are displayed in an order that matches that of the data in the legend.
Given the differences in methods of the computation of bothAandS, the values I measured are in a reasonable agreement with the NASA-Sloan Atlas. However, there is a notable incon- sistency in the values from both this work and NSA when compared with HT08, particularly in the case of the asymmetry parameter. It appears that these large discrepancies in the values ofAare likely to be a noise correction effect: when disregarding the noise correction term
(or, equivalently settingA=A+Ab g r) I obtained values within a similar range to HT08. This
suggests that the asymmetry measured by HT08 does not account for the contribution from the background asymmetry (or that the correction was calculated in a manner significantly different from my approach and that used by the authors of the NSA).
Figure 2.8 shows how the values ofSmeasured for the galaxies in the CIG sample changed when considering different variations of the definition of the parameter, and how they compare with the values found by HT08 and NSA. Generally, there appears to be a moderate correlation between the values measured in this work and those found in NSA, and they span similar ranges. Again, the correlation weakens when comparing the values of HT08 with NSA (even when disregarding the outliers with S>0.6). As there are a number of variables associated with the definition ofS, it is not as straight forward as in the case ofAto determine the main cause of the differences in the values obtained by different studies.
Summary
The tests outlined above have shown that the parameters are in a good agreement with litera- ture values, in particular, those for which the methods of computation used in this work are not considerably different from those used in the other studies (the r-band magnitude, surface- brightness profile, concentration index). The remaining parameters (Sérsic index, asymmetry, clumpiness) were found to agree with literature reasonably well, given the notable discrepan- cies in the computational methods. As the tested parameters were chosen such that to ensure the coverage of the code’s main components, the outcomes of the test can be extrapolated to the remaining elements of the analysis, and consequently, the code may be regarded as a reliable image analysis tool. The correct working of the code is further confirmed in Chap- ter 3, where the structural parameters computed for a sample of normal galaxies of different morphological types fall in the range of the expected values, based on previous studies.
2.6. Code testing
Figure 2.9:SDSS false colour images of all galaxies in the CIG sample ordered according to their central concentration (from highest to lowest, as measured by the concentration index (Section 2.4.2)). For each galaxy the values of the following parameters are displayed: Sérsic index (n), concentration index (C), asymmetry parameter (A), clumpiness parameter (S), reduced clumpiness parameter (Sc, with
Figure 2.10: SDSS r-band images of all galaxies in the CIG sample, cleaned of all potentially con- taminating sources that lie outside the boundaries defined by the detection masks (see Section 2.2.1). The galaxies are ordered by their central concentration (from highest to lowest, as measured by the concentration index (Section 2.4.2)). For each galaxy, the circular apertures defined by the following radii are shown: grey -r20; black -r80; blue -rma x; yellow - SDSS’ 1.5×rp; purple - SDSS’(1.5×rp)/5.
3
Shape asymmetry of galaxies with post-merger
signatures
To investigate the suitability of the new measure of morphological asymmetry introduced in this study I used it, along with the preexisting measures of galaxy structure described in Section 2.4, to analyse the SDSSr-band images of galaxies with and without morphological disturbance and features pointing to dynamical galaxy interactions. In this chapter, I present the results of the analysis. First, I investigate how the standard structural parameters mea- sured for morphologically disturbed galaxies compare with those found for normal early and late galaxy types (Section 3.2). Then, in Section 3.3, I discuss the advantage of using the newly introduced morphological indicator, the shape asymmetry, over the literature measures of galaxy structure in detecting faint asymmetric tidal features in the outskirts of galaxies. The results presented in this chapter, excluding Sections 3.3.1 and 3.3.2, are contained within Section 4 of Pawlik et al. (2016).
3.1
Sample selection
To find galaxies with disturbed morphologies within the SDSS catalogues, I pre-selected a representative sample of local (0.01 <z< 0.07) galaxies with spectroscopic signatures of a recent starburst (as described in detail in Section 4.1), with SDSS Petrosian magnitude within the range: 14.5<mr<17.7. Then, I visually examined the r-band as well as the composite false-colour images provided by the SDSS to select galaxies with various level of morphological disturbance and tidal features, pointing to an ongoing or past merger. The final sample consists of 70 objects including:
• 20 galaxies with highly disrupted morphologies featuring tidal signatures of a past (or, in a few cases, ongoing) major merger (MORPH1-20);
• 10 galaxies with moderate morphological disturbance, with no prominent tidal features but slight deviations from regular appearance (MORPH21-30);
• 10 galaxies with no signs of morphological disturbance (MORPH31-40).
It is important to note that the sample was selected for the purpose of method testing and calibration, rather than for a scientific study of galaxy structure and morphology, and is not representative of the entire population of local starburst and post-starburst galaxies (there is a notable fraction of ongoing mergers within the sample, which is a consequence of the selection criteria chosen specifically for purposes of the method testing).
In order to compare the values of the morphological and structural parameters obtained for the disturbed galaxies with those found for galaxies which do not show any sign of dis- turbance, I also included in the sample a control subset of normal galaxies selected using the same constraints on redshift and magnitude as above and additional cuts on the SDSSfracDev
parameter. ThefracDevparameter describes the fraction of the total galaxy light fit by the de Vaucouleurs profile (with the total light being represented by the model magnitude, computed from a linear combination of de Vaucouleurs and exponential fits to galaxy light profiles) and can be used to distinguish between early- and late-type morphologies. The subset of normal galaxies selected in that way includes: