New submission: Improving CLEAN for rotation measure synthesis

This week we submitted a new paper to A&A where we suggest a method for improving CLEAN images in the context of RM synthesis. The method allows one to make lower resolution images while obtaining better results than when using CLEAN alone and moreover it makes the results much less dependent on the choice of pixelization.

What we and many others have found is that RMCLEAN doesn't do such a great job at reconstructing the locations or fluxes of sources, especially when there are several of them close together. When writing the 3D CLEAN algorithm for Faraday synthesis we noticed that RMCLEAN doesn't even do that great with a single source unless it is located directly in the middle of an image pixel. The dynamic range in a CLEANed image is well-known to be limited due to the fact that you can't exactly model the location of a source in a pixelized image. You can partially overcome the issue by making the image have very high resolution, but especially for 3D imaging this becomes expensive both computationally and in terms of storage.

So we looked into this in a bit more detail, and devised a method for improving the CLEAN generated model using maximum likelihood (ML) estimation. The method is similar to others that have been suggested for aperture synthesis imaging. but it seems to have even more impact in the case of RM synthesis. In our testing, we found that the ML method dramatically reduces the error in measurements of both source location and flux. Somewhat surprisingly, we also found that increasing resolution doesn't reduce the errors in normal CLEAN images in the case where two sources are nearby to one another. However, he ML algorithm was able to get accurate results in such a case even in a low resolution image.

Ultimately we don't think that CLEAN (or any method that assumes the signal to be diagonal in pixel space) is the right approach in all cases, and we're actively working on new methods that take advantage of correlated structures in the data to help constrain reconstructions. Nevertheless, CLEAN is undoubtedly a useful algorithm in some circumstances, and together with this new algorithm it can give rather good results while being easy to implement and (relatively) computationally inexpensive.

The pre-print is available now on the arXiv. Give it a read if you want to learn more. And if you're interested in implementing the method for your own analysis, then be sure to stay tuned because I plan to release the code for this relatively soon.

An early look at LOFAR imaging results

The LOFAR station near Munich

Things are progressing quite nicely with the LOw Frequency ARray a.k.a. LOFAR. Despite the significant challenges involved with operating a radio telescope of its kind (maybe I'll post something about this one day), high quality data is now routinely being collected, and some very impressive images are being produced (see e.g. the ASTRON picture of the day from March 18th). Who knew that you could throw a bunch of relatively cheap dipole antennas on the ground and end up with a world class telescope? It's not been possible without a lot of hard work and ingenuity, I assure you. And while there are still many very complicated (and interesting) calibration and imaging issues left to solve, overall I would say that we are on the verge of producing scientific quality results.  

Don't believe me? Well, George Heald from ASTRON has just published a conference proceeding highlighting some of the recent achievements of a group of hardworking LOFAR commissioners. To this paper, I have contributed some results from our polarization commissioning efforts. Check it out. You can find it on the arXiv here: http://arxiv.org/abs/1106.3195

Faraday caustics paper submitted

Together with Henrik Junklewitz and Torsten Enßlin, I've just submitted a new paper to A&A introducing Faraday caustics to the world.  Never heard of them?  I don't blame you, we just coined the phrase.  So what are they?  They are singularities that appear in the Faraday spectrum, i.e. the distribution of polarized intensity as a function of Faraday depth (analogous to rotation measure).  These singularities are caused by reversals of the magnetic field direction along the line of sight.

Sounds a pretty obscure, I know, but there are two reasons why they are important to understand.  First, the RM Synthesis technique (used to measure Faraday spectra) is becoming very popular.  Anyone looking at the polarized emission from diffuse polarized sources, e.g. the synchrotron emission from the interstellar medium in the Milky Way, is bound to find these Faraday caustics in their data.  It's important to understand what these features are and where they come from in order to properly understand the results.  Furthermore we show how Faraday caustics can be used as a powerful tool for studying the structural and statistical properties of magnetic fields.  In this way, observations of Faraday caustics could be used to greatly improve our understanding of both the large and small-scale properties of the magnetic field in the Milky Way, for example.

If you are interested in learning more, our paper entitled "Faraday caustics: Singularities in the Faraday spectrum and their utility as probes of magnetic field properties" is available in pre-print now.

SS 433 jet evolution paper accepted!

This week I received word that a paper that I've written along with David Roberts and John Wardle, entitled "Structure and Magnetic Fields in the Precessing Jet System SS433 III. Evolution of the Intrinsic Brightness of the Jets from a Deep Multi-Epoch VLA Campaign" (whew... that's a mouthful), has been accepted for publication in the Astrophysical Journal.  It is tentatively scheduled to appear in the July 10, 2011, v735 - 2 issue so keep an eye out.  Of course, if you can't wait that long there is a pre-print version available.

As you might have gathered from the title, this is one in a series of papers on SS 433.  I am working on a follow up to this now where we will look at the images of linear polarization from the same data analyzed in paper III.  We hope to have it submitted by the Fall.  For reference, here are links to the first two papers in the series: Paper I, Paper II.