Tuesday, November 26, 2013

Wednesday, November 20, 2013

lit: Yegorova et al. 2007

A detailed investigation of the dependence of the slope of TFR on the radius where the velocity is measured. They find that the scatter is smallest at R = 0.6 R_opt (see fig.5).

Tuesday, November 19, 2013

lit: Some aspects of measurement error in linear regression of astronomical data, Kelly 2007

Kelly 2007:
I describe a Bayesian method to account for measurement errors in linear regression of astronomical data. The method allows for heteroscedastic and possibly correlated measurement errors and intrinsic scatter in the regression relationship. The method is based on deriving a likelihood function for the measured data, and I focus on the case when the intrinsic distribution of the independent variables can be approximated using a mixture of Gaussian functions. I generalize the method to incorporate multiple independent variables, non-detections, and selection effects (e.g., Malmquist bias).

LaTeX: \include vs. \input

I was annoyed by a page break that appears if I \include{a portion of my document}. Using \input{} instead solves this.

Monday, November 18, 2013

lit: Tonini et al. 2013, on physically meaningful radius for velocity measurements

They define the 'corrected' disk scale-length that includes the bulge potential. Pay attention to this article anyway -- they add a dynamical classificator dimension to the TFR, which is a little bit akin to what I'm doing.

Thursday, November 14, 2013

Daily paper: The Tully-Fisher relations of early-type spiral and S0 galaxies

I gave a talk at our group meeting yesterday, this was one of several useful suggestions and references I got. bWilliams et al. 2010, The Tully-Fisher relations of early-type spiral and S0 galaxies
Offsets which are ascribed to fading or brightening could in fact be due to systematic biases that differ between the measures of rotation used. For typical slopes of the TFR, a relatively small systematic difference in velocity of ∼0.1 dex introduces an offset in the TFR that is indistinguishable from a ∼ 1mag difference in luminosity.
Our conclusions in this section have three consequences for previous works that measure the TFR of S0 galaxies using stellar kinematics corrected for asymmetric drift (e.g. Neistein et al. 1999; Hinz et al. 2001, 2003, BAM06): (i) the asymmetric drift correction they use yields similar results to detailed Jeans modelling; (ii) the drift correction does not seem to introduce a systematic bias between spirals and S0s and, if applied to samples of both spirals and S0s, can indeed be used to compare the TFRs of the two classes; (iii) however, a TFR derived from asymmetric drift-corrected stellar kinematics cannot be directly compared to TFRs derived from global HI line widths or resolved emission line PVDs.
==== There's a really interesting section on the ADC, and a nice comparison of gas/HI/AD corrected stellar kinematics. Also I should check their section on TFR fitting, intrinsic scatter in it, etc.In addition, I ought to take a look at the references therein, esp. the introduction.

Tuesday, November 12, 2013

SQLITE: nested query for MoG

select avg(gc.r_mag) from luminosity_errors2 as gc where gc.califa_id in(select t.califa_id from tidal_influence as t where t.Ftop > -0 and t.Ftop <98)

Thursday, November 7, 2013

lit: Face-on TFR with IFU

lit: TFR at z=2.2 with SINFONI IFU

Cresci et al. 2013 -- they also use genetic algorithms for velocity field modelling.

lit: The power spectrum dependence of dark matter halo concentrations

lit: The baryonic Tully–Fisher relation and galactic outflows

Dutton and van den Bosch 2009, also see Dutton et al. 2011.

lit: testing CDM with low-mass TFR

lit: The baryonic Tully-Fisher relation and its implication for dark matter halos

Trachternach et al 2009: baryonic TFR as constraint of elongation of DM haloes.

lit: IFU observations of galaxies wrt TFR

Courteau 2003 is important: they observe 14 barred galaxies with Sparse Pak IFU in order to calibrate long-slit samples. To be discussed in talk/article!

lit: Skewness and kurtosis in BTFR

McGaugh 2012:
A persistent problem with interpreting astronomical data is whether Gaussian statistics actually apply. One reason for this is that systematic errors often dominate random ones. We can check whether this might be an issue here by examining higher order statistics like the skew (lopsidedness) and kurtosis (pointiness) of the distribution in the deviations perpendicular to the BTFR. If the data are well behaved (i.e., distributed normally), the skew and kurtosis should be small.

Monday, November 4, 2013

The difference between MCMC and simulations

from PSU astrostatistics lecture notes:
MCMC is a form of simulation, because MCMC generates random numbers and bases the inference on them. However, there are two important differences between MCMC and the simulation that we shall discuss here.
a) In MCMC we do not simulate from the target distribution directly. Instead, we simulate a Markov Chain that converges to the target distribution. Thus, MCMC performs approximate simulation.
b) The primary use of MCMC is in Bayesian statistics where we seek to simulate from the posterior distribution of the parameters. Classical simulation on the other hand simulates fresh data.