SINGS
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From SINGS (Spitzer Infrared Nearby Galaxies Survey): Isn’t it a beautiful Hubble tuning fork?
Boyle & Smith (1969)
The 2009 Physics Nobel is shared (along with Charles Kao, who is cited for suggesting optic fibers) by Willard Boyle and George Smith, inventors of the Charge-coupled Device.
The CCD, of course, is the workhorse of modern Astronomy. I cannot even imagine how things would be without it.
Goodness-of-fit tests
When it comes to applying statistics for measuring goodness-of-fit, the Pearson χ2 test is the dominant player in a race and the Kolmogorov-Smirnoff test statistic trails far behind. Although it seems almost invisible in this race, there are more various non-parametric statistics for testing goodness-of-fit and for comparing the sampling distribution to a reference distribution as legitimate race participants trained by many statisticians. Listing their names probably useful to some astronomers when they find the underlying assumptions for the χ2 test do not match the data. Perhaps, some astronomers want to try other nonparametric test statistics other than the K-S test. I’ve seen other test statistics in astronomical journals from time to time. Depending on data and statistical properties, one test statistic could work better than the other; therefore, it’s worthwhile to keep the variety in one’s mind that there are other tests beyond the χ2 test goodness-of-fit test statistic.
[MADS] Kalman Filter
I decide to discuss Kalman Filter a while ago for the slog after finding out that this popular methodology is rather underrepresented in astronomy. However, it is not completely missing from ADS. I see that the fulltext search and all bibliographic source search shows more results. Their use of Kalman filter, though, looked similar to the usage of “genetic algorithms” or “Bayes theorem.” Probably, the broad notion of Kalman filter makes it difficult my finding Kalman Filter applications by its name in astronomy since often wheels are reinvented (algorithms under different names have the same objective).
data analysis system and its documentation
So far, I didn’t complain much related to my “statistician learning astronomy” experience. Instead, I’ve been trying to emphasize how fascinating it is. I hope that more statisticians can join this adventure when statisticians’ insights are on demand more than ever. However, this positivity seems not working so far. In two years of this slog’s life, there’s no posting by a statistician, except one about BEHR. Statisticians are busy and well distracted by other fields with more tangible data sets. Or compared to other fields, too many obstacles and too high barriers exist in astronomy for statisticians to participate. I’d like to talk about these challenges from my ends.[1]
- This is quite an overdue posting. Links and associated content can be outdated.[↩]
To Become a Good Astronomer
By accident, a piece of paper was found from my old text book. I have no idea who wrote this, nor how old it is. Too old to be obsolete? But it has general description to become a good person and scientist
More on Space Weather
Thanks to a Korean solar physicist[1] I was able to gather the following websites and some relevant information on Space Weather Forecast in action, not limited to literature nor toy data.
- Space Weather Research Lab at NJIT
- SEEDS — Solar Eruptive Event Detection System at George Mason University.
- CACTUS A software package for ‘Computer Aided CME Tracking
- SRON in the Netherlands
- I must acknowledge him for his kindness and patience. He was my wikipedia to questions while I was studying the Sun.[↩]
[Books] Bayesian Computations
A number of practical Bayesian data analysis books are available these days. Here, I’d like to introduce two that were relatively recently published. I like the fact that they are rather technical than theoretical. They have practical examples close to be related with astronomical data. They have R codes so that one can try algorithms on the fly instead of jamming probability theories.
[MADS] compressed sensing
Soon it’ll not be qualified for [MADS] because I saw some abstracts with the phrase, compressed sensing from arxiv.org. Nonetheless, there’s one publication within refereed articles from ADS, so far.
http://adsabs.harvard.edu/abs/2009MNRAS.395.1733W.
Title:Compressed sensing imaging techniques for radio interferometry
Authors: Wiaux, Y. et al.…Continue reading»
[ArXiv] component separation methods
I happened to observe a surge of principle component analysis (PCA) and independent component analysis (ICA) applications in astronomy. The PCA and ICA is used for separating mixed components with some assumptions. For the PCA, the decomposition happens by the assumption that original sources are orthogonal (uncorrelated) and mixed observations are approximated by multivariate normal distribution. For ICA, the assumptions is sources are independent and not gaussian (it grants one source component to be gaussian, though). Such assumptions allow to set dissimilarity measures and algorithms work toward maximize them.
[MADS] ARCH
ARCH (autoregressive conditional heteroscedasticity) is a statistical model that considers the variance of the current error term to be a function of the variances of the previous time periods’ error terms. I heard that this model made Prof. Engle a Nobel prize recipient.
[ArXiv] Statistical Analysis of fMRI Data
[arxiv:0906.3662] The Statistical Analysis of fMRI Data by Martin A. Lindquist
Statistical Science, Vol. 23(4), pp. 439-464
This review paper offers some information and guidance of statistical image analysis for fMRI data that can be expanded to astronomical image data. I think that fMRI data contain similar challenges of astronomical images. As Lindquist said, collaboration helps to find shortcuts. I hope that introducing this paper helps further networking and collaboration between statisticians and astronomers.
List of similarities
[MADS] Kriging
Kriging is the first thing that one learns from a spatial statistics course. If an astronomer sees its definition and application, almost every astronomer will say, “Oh, I know this! It is like the 2pt correlation function!!” At least this was my first impression when I first met kriging.
There are three distinctive subjects in spatial statistics: geostatistics, lattice data analysis, and spatial point pattern analysis. Because of the resemblance between the spatial distribution of observations in coordinates and the notion of spatially random points, spatial statistics in astronomy has leaned more toward the spatial point pattern analysis than the other subjects. In other fields from immunology to forestry to geology whose data are associated spatial coordinates of underlying geometric structures or whose data were sampled from lattices, observations depend on these spatial structures and scientists enjoy various applications from geostatistics and lattice data analysis. Particularly, kriging is the fundamental notion in geostatistics whose application is found many fields.
Beyond simple models-New methods for complex data
This is a special session at the January 2010 meeting of the AAS. It is scheduled for the afternoon of Thursday, Jan 7, 2-3:30pm.
Abstracts are due Sep 17.
Meeting Justification
We propose to highlight the growing use of ‘non-parametric’ techniques to distill meaningful science from today’s astronomical data. Challenges range from Kuiper objects to cosmology. We have chosen just a few ‘teaching’ examples from this lively interdisciplinary area.
2010 SBSS STUDENT PAPER COMPETITION
The Section on Bayesian Statistical Science (SBSS) of the American Statistical Association (ASA) would like to announce its 2010 student paper competition. Winners of the competition will receive partial support for attending the 2010 Joint Statistical Meetings (JSM) in Vancouver, BC.
Eligibility
The candidate must be a member of SBSS (URL: www.amstat.org/membership/chapsection.pdf) or ISBA (International Society for Bayesian Analysis). Those candidates who have previously received travel support from SBSS are not eligible to participate. In addition, the candidate must be a full-time student (undergraduate, Masters, or Ph.D.) on or after September 1, 2009.
A manuscript, suitable for journal submission, is required for entry. The candidate must be the lead author on the paper, and hold the primary responsibility for the research and write-up.
The candidate must have separately submitted an abstract for JSM 2010 through the regular abstract submission process, to present applied, computational, or theoretical Bayesian work. Papers should be submitted for presentation at the JSM as topic contributed or invited papers. Those papers not already a part of a session should be submitted online using the following settings:
(at URL: www.amstat.org/meetings/jsm/2010/index.cfm?fuseaction=abstracts):
* Abstract Type: Topic contributed
* Sub Type: Papers
* Sponsor: Section on Bayesian Statistical Science
* Organizer: Alyson Wilson
* Organizer e-mail: agw -at- iastate.edu
Application Process
The deadline for application is Feb. 1 (same as the JSM 2010 abstract submission deadline). A formal application including the following materials should be emailed to Prof. Vanja Dukic (vanja -at- uchicago.edu):
a) CV
b) Abstract number (from the ASA JSM 2010 abstract submission)
c) Letter from the major professor (advisor) or faculty co-author, verifying the student status of the candidate, and briefly describing the candidate’s role in the research and writing of the paper
d) The manuscript, suitable for journal submission, in .pdf format.
Selection of Winners
Papers will be reviewed by a committee determined by the officers of the SBSS. Criteria for selection will include, but are not limited to, significance and potential impact of the research. Decisions of the committee are final, and will be announced in the Spring before the JSM.
Prizes
Prizes will consist of a certificate to be presented at the SBSS section meeting and partial support (up to $1000) for attending the JSM. Please note that the awards may be unable to cover the entirety of any winner’s travel, so winning candidates may need to supplement the SBSS award with other funds. To receive a monetary prize, the winner will need to provide proof of membership and submit travel receipts to the SBSS treasurer after the JSM.