The AstroStat Slog

A few scientists in our group work on estimating the intensities of gamma ray observations from sky surveys. This work distinguishes from typical image processing which mostly concerns the point estimation of intensity at each pixel location and the size of overall white noise type error. Often times you will notice from image processing that the orthogonality between errors and sources, and the white noise assumptions. These assumptions are typical features in image processing utilities and modules. On the other hand, CHASC scientists relate more general and broad statistical inference problems in estimating the intensity map, like intensity uncertainties at each point and the scientifically informative display of the intensity map with uncertainty according to the Poisson count model and constraints from physics and the instrument, where the field, multiscale modeling is associated. Continue reading ‘[MADS] multiscale modeling’ »

People of experience would say very differently and wisely against what I’m going to discuss now. This post only combines two small cross sections of each branch of two trees, astronomy and statistics. Continue reading ‘survey and design of experiments’ »

One of the speakers from the google talk series exemplified model based clustering and mentioned the likelihood ratio test (LRT) for defining the number of clusters. Since I’ve seen the examples of ill-mannerly practiced LRTs from astronomical journals, like testing two clusters vs three, or a higher number of components, I could not resist indicating that the LRT is improperly used from his illustration. As a reply, the citation regarding the LRT was different from his plot and the test was carried out to test one component vs. two, which closely observes the regularity conditions. I was relieved not to find another example of the ill-used LRT. Continue reading ‘The LRT is worthless for …’ »

It’s a report about the job market in US.

[astro-ph:0712.2820] The Production Rate and Employment of Ph.D. Astronomers T.S. Metcalfe

Continue reading ‘[ArXiv] Astronomy Job Market in US’ »

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.
Continue reading ‘Arrogant?’ »

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.
Continue reading ‘[ArXiv] NGC 6397 Deep ACS Imaging, Aug. 29, 2007’ »

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.
Continue reading ‘[ArXiv] Numerical CMD analysis, Aug. 28th, 2007’ »

1. Numerical methods of star formation history measurement and applications to seven dwarf spheroidals,Dolphin (2002), MNRAS, 332, p. 91[↩]

Much to everybody’s surprise, they let some astronomers into the recently concluded Joint Statistical Meeting at Salt Lake City, UT. There were two three astrostat sessions: [#45 on Probing the Universe with Nonparametric Methods,] #367 on Bayesian Applications in Astronomy and Physics (chaired by David van Dyk), and #411 on Image Analysis in Solar- and Astro-physics (chaired by Yaming Yu and Thomas Lee). Both [of the latter] sessions were dominated by presentations from CHASC collaborators.

From arxiv/astro-ph:0707.1891v1
The Geneva-Copenhagen Survey of the Solar neighborhood II. New uvby calibrations and rediscussion of stellar ages, the G dwarf problem, age-metalicity diagram, and heating mechanisms of the disk by Holmberg, Nordstrom, and Andersen

Researchers, including scientists from CHASC, working on color magnitude diagrams to infer ages, metalicities, temperatures, and other physical quantities of stars and stellar clusters may find this paper useful.
Continue reading ‘[ArXiv] Geneva-Copenhagen Survey, July 13, 2007’ »

From arxiv/astro-ph, arXiv:0706.2590v1 Extreme Value Theory and the Solar Cycle by Ramos, A. This paper might drag a large attention from CHASC members.
Continue reading ‘[ArXiv] Solar Cycle, June 18, 2007’ »

From arxiv/astro-ph:0706.1988,
Lectures on Astronomy, Astrophysics, and Cosmology looks helpful to statisticians who like to know astronomy, astrophysics, and cosmology. The lecture note starts from introducing fundamentals of astronomy, UNITS!!!, and its history. It also explains astronomical measures such as distances and their units, luminosity, and temperature; HR diagram (astronomers’ summary diagram); stellar evolution; and relevant topics in cosmology. At least, a third of the article will be useful to grasp a rough idea of astronomy as a scientific subject beyond colorful pictures. Statisticians who are keen to cosmology are recommended to read beyond.

This is not a high energy lecture note; therefore, statisticians interested in high energy are encouraged to visit Astro Jargon for Statisticians and CHASC.

Leo Breiman (1928-2005) was one of the most dominant statisticians from the 20th century. He was well known for his textbook in probability theory as well as his contributions to the machine learning, such as CART (Classification and Regression Tree), bagging (bootstrap aggregation), and Random Forest. He was the founding father of statistical machine learning. His works can be found from http://www.stat.berkeley.edu/~breiman/

An excerpt from “A Conversation with Leo Breiman,” from Statistical Science, by Richard Olshen (2001), 16(2), pp. 184–198, casts a second thought on the direction of statistical researches:
Continue reading ‘An excerpt from “A Conversation with Leo Breiman”’ »

The Statistics Jargon for Astronomers has revived (Jan. 2007) but now, it is barely breathing. Effortlessly search engines and Internet encyclopedia provide details on statistical terminologies for astronomers, which cast the largest difficulty on this jargon website. We urge both astronomers and statisticians’ contributions for compiling this lexicon and their discussions on both subjects.