The AstroStat Slog

From arxiv/astro-ph:0709.1144v1:
Cosmic Microwave Background Statistics for a Direction-Dependent Primordial Power Spectrum by A. R. Pullen and M. Kamionkowski

The authors developed cosmic microwave background statistics for a primordial power spectrum, motivated from the needs of testing the cosmological common assumption, i.e. the statistical isotropy of primordial perturbations. This statistics is for a primordial power spectrum, depending on the direction and the magnitude of the Fourier wavevector. Statistically speaking, the most interesting part is their construction of the minimum-variance estimators for the coefficients of a spherical-harmonic expansion of the direction-dependence of the primordial power spectrum.

From arxiv/astro-ph:0708.1208v1:
The measurement errors in the Swift-UVOT and XMM-OM by N.P.M. Kuin and S.R. Rosen

The probability distribution of photon counts from the Optical Monitor on XMM Newton satellite (XMM-OM) and the UVOT on the Swift satellite follows a binomial distribution due to detector characteristics. Incident count rate was derived as a function of the measured count rate, which was shown to follow a binomial distribution.
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arXiv:0709.1067v1 : Wrong Priors (Carlos C. Rodriguez)

This came through today on astro-ph, suggesting that we could be choosing priors better than we do, and in fact that we generally do a very bad job of it. I have been brought up to believe that, like points in Whose Line Is It Anyway, priors don’t matter (unless you have very little data), so I am somewhat confused. What is going on here?

Google Sky is good for a quick look “what’s that you just saw over there?”, but not for anything more than that. Not yet anyway. Mind you, I think it is a good thing. It is easy to use, and definitely worth a look as an astronomy popularization tool. But there are a number of astro visualization programs that can (so to speak) beat the pants off Google Sky with one hand tied behind the back. Check these out (all open source): …Continue reading»

BEHR (Bayesian Estimation of Hardness Ratios) is a C program designed to compute hardness ratios – summary statistics of a spectrum for faint X-ray sources – while explicitly modeling the arrival of photons as an inhomogeneous Poisson process and properly accounting for background contamination.

In the previous versions of BEHR, users are allowed to choose only the gamma prior distribution that is parametrized in terms of an index (shape) parameter and a scale parameter. But there was need for using a tabulated prior distribution that does not necessarily look like a gamma distribution. The use of a tabulated prior distribution gives us two advantages:
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From arxiv/math.st: 0708.0499v1
Inference for mixtures of symmetric distributions by Hunter, Wang, and Hettmansperger, Annals of Statistics, 2007, Vol.35(1), pp.224-251.
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Ah..Sky in Google Earth made an arxiv appearance [arxiv/astro-ph:0709.0752], Sky in Google Earth: The Next Frontier in Astronomical Data Discovery and Visualization by R. Scranton et al.

I once talked about the relationship between astronomers and statisticians in the slog posting Data Doctors. To astronomers, statisticians are assistants. Statisticians are just helping astronomical data analysis with statistically limited eyes. Less frequently statistical improvements and modification occurred in the astronomical society through collaborations with statisticians compared to other fields.
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There are two statistics papers on astro-ph. Check them out:

1/ http://arxiv.org/abs/0709.0596

Title: Bayesian Inversion of Stokes Profiles

Authors: A. Asensio Ramos (1), M. J. Martinez Gonzalez (2), J. A. Rubino-Martin (1) ((1) IAC, (2) LERMA)

Comments: 15 pages, 12 figures, accepted for publication in A&A

2/ http://arxiv.org/abs/0709.0711

Title: Bayesian posterior classification of planetary nebulae according to the Peimbert types

Authors: C. Quireza (1), H.J. Rocha-Pinto (2), W.J. Maciel (3) ((1) Observatorio Nacional/MCT, (2) Observatorio do Valongo/UFRJ, (3) Instituto de Astronomia, Geofisica e Ciencias Atmosfericas/USP)

Comments: 26 pages, 4 figures, 6 tables. Accepted for publication in Astronomy and Astrophysics.

From arxiv/astro-ph:0708.4030v1
Deep ACS Imaging in the Globular Cluster NGC 6397: The Cluster Color Magnitude Diagram and Luminosity Function by H.B. Richer et.al

This paper presented an observational study of a globular cluster, named NGC 6397, enhanced and more informative compared to previous observations in a sense that 1) a truncation in the white dwarf cooling sequence occurs at 28 magnitude, 2) the cluster main sequence seems to terminate approximately at the hydrogen-burning limit predicted by two independent stellar evolution models, and 3) luminosity functions (LFs) or mass functions (MFs) are well defined. Nothing statistical, but the idea of defining color magnitude diagrams (CMDs) and LFs described in the paper, will assist developing suitable statistics on CMD and LF fitting problems in addition to the improved measurements (ACS imaging) of stars in NGC 6397.
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From arxiv/astro-ph:0708.4274v1
Comparison of decision tree methods for finding active objects by Y. Zhao and Y. Zhang

The authors (astronomers) introduced and summarized various decision three methods (REPTree, Random Tree, Decision Stump, Random Forest, J48, NBTree, and AdTree) to the astronomical community.
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Once again, the middle of a recent (Aug 30-31, 2007) argument within CHASC, on why physicists and astronomers view “3 sigma” results with suspicion and expect (roughly) > 5 sigma; while statisticians and biologists typically assume 95% is OK:

David van Dyk (representing statistics culture):

Can’t you look at it again? Collect more data?

Vinay Kashyap (representing astronomy and physics culture):

…I can confidently answer this question: no, alas, we usually cannot look at it again!!

Ah. Hmm. To rephrase [the question]: if you have a “7.5 sigma” feature, with a day-long [imaging Markov Chain Monte Carlo] run you can only show that it is “>3sigma”, but is it possible, even with that day-long run, to tell that the feature is really at 7.5sigma — is that the question? Well that would be nice, but I don’t understand how observing again will help?

David van Dyk :

No one believes any realistic test is properly calibrated that far into the tail. Using 5-sigma is really just a high bar, but the precise calibration will never be done. (This is a reason not to sweet the computation TOO much.)

Most other scientific areas set the bar lower (2 or 3 sigma) BUT don’t really believe the results unless they are replicated.

My assertion is that I find replicated results more convincing than extreme p-values. And the controversial part: Astronomers should aim for replication rather than worry about 5-sigma.

From arxiv/astro-ph:0708.3758v1
Numerical Color-Magnitude Diagram Analysis of SDSS Data and Application to the New Milky Way Satellites by J. T. A. de Jong et. al.

The authors applied MATCH (Dolphin, 2002^[1] -note that the year is corrected) to M13, M15, M92, NGC2419, NGC6229, and Pal14 (well known globular clusters), and BooI, BooII, CvnI, CVnII, Com, Her, LeoIV, LeoT, Segu1, UMaI, UMaII and Wil1 (newly discovered Milky Way satellites) from Sloan Digital Sky Survey (SDSS) to fit Color Magnitude diagrams (CMDs) of these stellar clusters and find the properties of these satellites.
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1. Numerical methods of star formation history measurement and applications to seven dwarf spheroidals,Dolphin (2002), MNRAS, 332, p. 91[↩]

SLAC is hosting a workshop Nov 8-9, 2007, on the Future of Very High-Energy Gamma-Ray Astronomy. …Continue reading»

These are from two lively CHASC discussions on classification, or cluster analysis. The first was on Feb 7, 2006; the continuation on Dec 12, 2006, at the Harvard Statistics Department, as part of Stat 310 .

David van Dyk:

Don’t demand too much of the classes. You’re not going to say that all events can be well-classified…. It’s more descriptive. It gives you places to look. Then you look at your classes.

Xiao Li Meng:

Then you’re saying the cluster analysis is more like -

David van Dyk:

It’s really like you have a propsal for classes. You then investigate the physical processes more thoroughly. You may have classes that divide it [up]

……

David van Dyk:

But it can make a difference, where you see the clusters, depending on your [parameter] transformation.You can squish the white spaces, and stretch out the crowded spaces; so it can change where you think the clusters are.

Aneta Siemignowska:

But that is interesting.

Andreas Zezas:

Yes, that is very interesting.

These are particularly in honor of Hyunsook Lee‘s recent posting of Chattopadhyay et. al.’s new work about possible intrinsic classes of gamma-ray bursts. Are they really physical classes — or do they only appear to be distinct clusters because we view them through the “squished” lens (parameter spaces) of our imperfect instruments?