UR: Create deeper VLA images by stacking

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Omar French

University of Maryland, Baltimore County

This guest post was written by Omar French, a fourth year physics student at the University of Maryland, Baltimore County (UMBC). He completed this research under the supervision of Dr Eileen Meyer, Assistant Professor of Physics at UMBC. He presented these results at the 237th meeting of the American Astronomical Society.

A great debate in astrophysics is the nature of the high energy emission mechanisms (optics / x-rays) that generate the huge jets of plasma which emanate from the active galaxy nuclei. One of the main obstacles to improving our understanding of jets is the lack of high-quality radio images, crucial for plotting the structure of the jet and estimating the magnetic field. A common technique for creating these higher sensitivity radio images is to stack many images together (theoretically, the thermal noise of the images should evolve with t-1/2, where t is the sweep time). In this project, radio images are obtained from Very Large Array (VLA) archive data.

Stacking Images is laborious to do manually and therefore it is desirable to automate. That being said, until proven guilty, automating certain processes tends to produce less precise images than doing it “by hand” without a script. To test this, I wrote a script that fully automates the entire image stacking process. Notably, we have found that the noise of the image scales roughly in accordance with t-1/2, which means that automating this process is viable and is certainly worth doing for large sample sizes (see image below). With the script, one can spend five minutes of one’s time stacking hundreds of images, making this process of unveiling weak plasma effortless.

Plot of how RMS (a quantitative measure of noise) varies with number of images stacked, stacked in order of increase in RMS. As shown, assuming similar integration times for each input image, RMS decreases roughly in accordance with t-1/2.

Astrobite edited by: Haley wahl

Featured Image Credit: Omar French

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