lit: Rotational support in galaxies

I was wondering how to derive v_rot/sigma ratio for my sample, here is a review: Rotation and anisotropy of galaxies revisited, by J. Binney.

Matplotlib: strings as axis labels

I wanted to make a plot that shows the relation of some galaxy properties vs. their Hubble subtype. The subtypes were saved as strings at my database, I selected them, converted back to numbers. Why back? Because some colleagues of mine, who think in words, classified all the 900 galaxies by their Hubble types, then their classifications were converted into numbers, means and probabilities calculated, and converted back to types.
Anyway, I wanted to have types on my axis, so I did it thus. Here's the plot:

ds9: an alias for multi-extension data cubes

Since I chronically cannot remember the command-line options to ds9, I added this to my ~/.bashrc: alias meds9=' -scale mode 99.5 -cmap Heat -medatacube'

lit: Intrinsic properties of SDSS galaxies

Maller et al.. It's important in many other ways, but here is an interesting excerpt on why TF relation is different:
However, with the notable exception of the Tully-Fisher relation these distributions and relations are traditionally measured in terms of the observed properties of galaxies. That is, the measurements used are K-corrected and corrected for foreground dust extinction, but no correction is attempted to compensate for the viewing angle from which the galaxies are observed. In contrast, the Tully-Fisher relation is not a relationship between a galaxy's observed luminosity and rotation velocity, but a relation between a galaxy luminosity and rotation velocity corrected for inclination. The inclination correction attempts to recover the intrinsic properties of a galaxy and not properties that are measured because of the particular angle from which the galaxy is viewed. For a long time there was great controversy over whether spiral galaxies are optically thin or optically thick (e.g., Holmberg 1958; Disney et al. 1989), but in the early 1990s it was established that galaxies become redder and fainter as they are inclined (e.g., Burstein et al. 1991).

lit review: a generalization of photometry to the higher moments of the line-of-sight velocity distribution

by Krajnovic et al.. This will probably be one of the important papers when writing: an interesting breakdown and discussion of velocity map moments, also many useful references (esp. Schoenmakers et al. (1997), the degeneracy of q, center and systemic velocity v0.
We have presented a generalization of surface photometry to the higher-order moments of the LOSVD, observed with integral-field spectrograph. We call our method kinemetry. For even moments of the LOSVD, kinemetry reduces to photometry. For odd moments, kinemetry is based on the assumption that it is possible to define an ellipse such that the kinematic profile extracted along the ellipse can be well described by a simple cosine law. This assumption is satisfied in the case of simple axisymmetric rotators.

Half-light radii to scale lengths

more than is needed.

lit: Tully-Fisher relation in SDSS bands

Pizagno et al., attention-worthy. After skimming through it, I had an idea of running GALFIT through my sample and getting better values of b/a ratios and PA's. I have a pretty good value of the sky, so why not model it?

Bulges and ellipticals

I read an interesting article on the train, Galaxy Bulges and Elliptical Galaxies, lecture notes by D. Gadotti. Dimitri had helped us a lot with the MICE decomposition.
He sums up a great deal of recent research regarding the structure of galaxies. A few main points:

• the components that we call bulges are not necessarily old 'scaled down' ellipticals as we often hear: there are classical bulges, disk-like bulges, 'box-peanuts', which may coexist or overlap.
• 'box-peanuts' are thought to be the inner parts of bars, not separate components
• classical bulges are kinematically hot, i.e. supported by their velocity dispersion. disk-like bulges are kinematically cold, i.e. supported by their rotation velocity.
• there's an interesting section on photometric bulge recognition, and why the arbitrary cut on n=2 is what it is, arbitrary. I tend to believe that any classification has to be data-driven, so I'll keep the alternative suggestions in mind.
• another interesting part is about the scaling relations, i.e. the fundamental plane and F-J relation. I didn't know that the FP can be derived directly from the virial theorem!
• F-J and L-size relations seem to be nonlinear, according to the data.

The only thing I'm not sure about is the use of 2D K-S test, an enlightening read otherwise.