The AstroStat Slog

The Solar Dynamics Observatory, which promises a flood of data on the Sun, was launched today from Cape Kennedy.

Among billion objects in our Galaxy, outside the Earth, our Sun drags most attention from astronomers. These astronomers go by solar physicists, who enjoy the most abundant data including 400 year long sunspot counts. Their joy is not only originated from the fascinating, active, and unpredictable characteristics of the Sun but also attributed to its influence on our daily lives. Related to the latter, sometimes studying the conditions on the Sun is called space weather forecast. Continue reading ‘space weather’ »

I’ve been complaining about how one can do machine learning on solar images without a training set? (see my comment at the big picture). On the other hand, I’m also aware of challenges in astronomy that data (images) cannot be transformed freely and be fed into standard machine learning algorithms. Tailoring data pipelining, cleaning, and processing to currently existing vision algorithms may not be achievable. The hope of automatizing the detection/identification procedure of interesting features (e.g. flares and loops) and forecasting events on the surface of the Sun is only a dream. Even though the level of image data stream is that of tsunami, we might have to depend on human eyes to comb out interesting features on the Sun until the new paradigm of automatized feature identification algorithms based on a single image i.e. without a training set. The good news is that human eyes have done a superb job! Continue reading ‘An excerpt from …’ »

Our hometown rag (the Boston Globe) runs an occasional series of photo collections that highlight news stories called The Big Picture. This week, they take a look at the Sun: http://www.boston.com/bigpicture/2008/10/the_sun.html

The pictures come from space and ground observatories, from SoHO, TRACE, Hinode, STEREO, etc. Goes without saying, the images are stunning, and some are even animated. The real kicker is that images such as these are being acquired by the hundreds, every hour upon the hour, 24/7/365.25 . It is like sipping from a firehose. Nobody can sit there and look at them all, so who knows what we are missing out on. Can statistics help? Can we automate a statistically robust “interestingness” criterion to filter the data stream that humans can then follow up on?

As Prof. Speed said, PCA is prevalent in astronomy, particularly this week. Furthermore, a paper explicitly discusses R, a popular statistics package. Continue reading ‘[ArXiv] 2nd week, June 2008’ »