Posts tagged ‘Tutorial’

#### 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] Continue reading ‘data analysis system and its documentation’ »

1. This is quite an overdue posting. Links and associated content can be outdated.[]

#### Where is ciao X ?

##### X={ primer, tutorial, cookbook, Introduction, guidebook, 101, for dummies, …}

I’ve heard many times about the lack of documentation of this extensive data analysis system, ciao. I saw people still using ciao 3.4 although the new version 4 has been available for many months. Although ciao is not the only tool for Chandra data analysis, it was specifically designed for it. Therefore, I expect it being used frequently with popularity. But the reality is against my expectation. Whatever (fierce) discussion I’ve heard, it has been irrelevant to me because ciao is not intended for statistical analysis. Then, out of sudden, after many months, a realization hit me. ciao is different from other data analysis systems and softwares. This difference has been a hampering factor of introducing ciao outside the Chandra scientist community and of gaining popularity. This difference was the reason I often got lost in finding suitable documentations. Continue reading ‘Where is ciao X ?’ »

#### [tutorial] multispectral imaging, a case study

Without signal processing courses, the following equation should be awfully familiar to astronomers of photometry and handling data:
$$c_k=\int_\Lambda l(\lambda) r(\lambda) f_k(\lambda) \alpha(\lambda) d\lambda +n_k$$
Terms are in order, camera response (c_k), light source (l), spectral radiance by l (r), filter (f), sensitivity (α), and noise (n_k), where Λ indicates the range of the spectrum in which the camera is sensitive.
Or simplified to $$c_k=\int_\Lambda \phi_k (\lambda) r(\lambda) d\lambda +n_k$$
where φ denotes the combined illuminant and the spectral sensitivity of the k-th channel, which goes by augmented spectral sensitivity. Well, we can skip spectral radiance r, though. Unfortunately, the sensitivity α has multiple layers, not a simple closed function of λ in astronomical photometry.
Or $$c_k=\Theta r +n$$
Inverting Θ and finding a reconstruction operator such that r=inv(Θ)c_k leads spectral reconstruction although Θ is, in general, not a square matrix. Otherwise, approach from indirect reconstruction. Continue reading ‘[tutorial] multispectral imaging, a case study’ »

#### Signal Processing and Bootstrap

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.

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’ »

#### [ArXiv] 3rd week, Dec. 2007

The paper about the Banff challenge [0712.2708] and the statistics tutorial for cosmologists [0712.3028] are the personal recommendations from this week’s [arXiv] list. Especially, I’d like to quote from Licia Verde’s [astro-ph:0712.3028],

In general, Cosmologists are Bayesians and High Energy Physicists are Frequentists.

I thought it was opposite. By the way, if you crave for more papers, click Continue reading ‘[ArXiv] 3rd week, Dec. 2007’ »

#### Learning R

R is a programming language and software for statistical computing and graphics. It is the most popular tool for statisticians and a widely used software for statistical data analysis thanks to the fact that its source code is freely available and it is fairly easy to access from installation to theoretical application.

Most of information about R can be found at R Project including the software itself and many add-on packages. These individually contributed packages serve particular statistical interests of their users. The documentation menu on the website and each packages contain extensive documentations of how-to’s. Some large packages include demos so that following the scripts in a demo makes R learning easy.