The AstroStat Slog

A Fast Thresholded Landweber Algorithm for Wavelet-Regularized Multidimensional Deconvolution
Vonesch and Unser (2008)
IEEE Trans. Image Proc. vol. 17(4), pp. 539-549

Quoting the authors, I also like to say that the recovery of the original image from the observed is an ill-posed problem. They traced the efforts of wavelet regularization in deconvolution back to a few relatively recent publications by astronomers. Therefore, I guess the topic and algorithm of this paper could drag some attentions from astronomers. Continue reading ‘Wavelet-regularized image deconvolution’ »

One of [ArXiv] papers from yesterday whose title might drag lots of attentions from astronomers. Furthermore, it’s a short paper.
[arxiv:math.CO:0905.0483] by Harmany, Marcia, and Willet.
Continue reading ‘[ArXiv] Sparse Poisson Intensity Reconstruction Algorithms’ »

Eight astro-ph papers and two statistics paper are listed this week. One statistics paper discusses detecting filaments and the other talks about maximum likelihood estimation of satellite images (clouds). Continue reading ‘[ArXiv] 4th week, May 2008’ »

The most widely used tool for detecting sources in X-ray images, especially Chandra data, is the wavelet-based wavdetect, which uses the Mexican Hat (MH) wavelet. Now, the MH is not a very popular choice among wavelet aficianados because it does not form an orthonormal basis set (i.e., scale information is not well separated), and does not have compact support (i.e., the function extends to inifinity). So why is it used here?
Continue reading ‘Mexican Hat [EotW]’ »

Since I learned Hubble’s tuning fork^[1] for the first time, I wanted to do classification (semi-supervised learning seems more suitable) galaxies based on their features (colors and spectra), instead of labor intensive human eye classification. Ironically, at that time I didn’t know there is a field of computer science called machine learning nor statistics which do such studies. Upon switching to statistics with a hope of understanding statistical packages implemented in IRAF and IDL, and learning better the contents of Numerical Recipes and Bevington’s book, the ignorance was not the enemy, but the accessibility of data was. Continue reading ‘[ArXiv] 5th week, Apr. 2008’ »

1. Wikipedia link: Hubble sequence[↩]

I’m very curious how astronomers began to use Monte Carlo Markov Chain instead of Markov chain Monte Carlo. The more it becomes popular, the more frequently Monte Carlo Markov Chain appears. Anyway, this week, I added non astrostatistical papers in the list: a tutorial, big bang, and biblical theology. Continue reading ‘[ArXiv] 1st week, Apr. 2008’ »

Some statistics papers were listed at the top, of which topics would interest some slog subscribers.
Continue reading ‘[ArXiv] 5th week, Jan. 2008’ »

Astronomers have developed their ways of processing signals almost independent to but sometimes collaboratively with engineers, although the fundamental of signal processing is same: extracting information. Doubtlessly, these two parallel roads of astronomers’ and engineers’ have been pointing opposite directions: one toward the sky and the other to the earth. Nevertheless, without an intensive argument, we could say that somewhat statistics has played the medium of signal processing for both scientists and engineers. This particular issue of IEEE signal processing magazine may shed lights for astronomers interested in signal processing and statistics outside the astronomical society.

IEEE Signal Processing Magazine Jul. 2007 Vol 24 Issue 4: Bootstrap methods in signal processing

This link will show the table of contents and provide links to articles; however, the access to papers requires IEEE Xplore subscription via libraries or individual IEEE memberships). Here, I’d like to attempt to introduce some articles and tutorials.
Continue reading ‘Signal Processing and Bootstrap’ »

There’s only one day in the first week of December with no preprint appearance. Dubbing the week of Dec. 2nd as the first week is hoped to be accepted. Continue reading ‘[ArXiv] 1st week, Dec. 2007’ »

Astronomers are hard working people, day and night, weekend and weekdays, 24/7, etc. My vacation delayed this week’s posting, not astronomers nor statisticians. Continue reading ‘[ArXiv] 5th week, Nov. 2007’ »

There should be at least one paper that drags your attention. Various statistics topics appeared in astro-ph this week.
Continue reading ‘[ArXiv] 2nd week, Nov. 2007’ »

My friend’s blog led me to Terrence Tao’s blog. A mathematician writes topics of applied mathematics and others. A glance tells me that all postings are well written. Especially, compressed sensing and single pixel cameras drags my attention more because the topic stimulates thoughts of astronomers in virtual observatory^[1] and image processing^[2] (it is not an exaggeration that observational astronomy starts with taking pictures in a broad sense) and statisticians in multidimensional applications, not to mention engineers in signal and image processing. Continue reading ‘compressed sensing and a blog’ »

1. see the slog posting “Virtual Observatory”[↩]
2. see the slog posting “The power of wavedetect”[↩]

wavdetect is a wavelet-based source detection algorithm that is in wide use in X-ray data analysis, in particular to find sources in Chandra images. It came out of the Chicago “Beta Site” of the AXAF Science Center (what CXC used to be called before launch). Despite the fancy name, and the complicated mathematics and the devilish details, it is really not much more than a generalization of earlier local cell detect, where a local background is estimated around a putative source and the question is asked, is whatever signal that is being seen in this pixel significantly higher than expected? However, unlike previous methods that used a flux measurement as the criterion for detection (e.g., using signal-to-noise ratios as proxy for significance threshold), it tests the hypothesis that the observed signal can be obtained as a fluctuation from the background. Continue reading ‘The power of wavdetect’ »