UR: Creating deeper VLA images by stacking




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Omar French

College of Maryland, Baltimore County

This visitor submit was written by Omar French, a fourth-year scholar learning Physics on the College of Maryland, Baltimore County (UMBC). He accomplished this analysis below Dr. Eileen Meyer, an assistant professor of physics at UMBC. He has introduced these outcomes on the 237th assembly of the American Astronomical Society.

A fantastic debate in astrophysics is the character of the high-energy (optical/X-ray) emission mechanisms that generate the big jets of plasma that emanate from the nuclei of active galaxies. One of many main obstacles to bettering our understanding of jets is the dearth of high-quality radio pictures, that are essential for tracing the jet construction and estimating the magnetic subject. A standard approach to create these higher-sensitivity radio pictures is to stack many pictures collectively (theoretically, the thermal noise of pictures is anticipated to scale with t-1/2, the place t is scanning time). On this mission, radio pictures are obtained from archival Very Massive Array (VLA) knowledge. 

Image stacking is laborious to do manually and subsequently is fascinating to automate. That being stated, till proven in any other case, automating sure processes tends to supply much less correct pictures than doing so “by-hand” with out a script. To check this, I’ve written a script that totally automates all the means of picture stacking. Notably, now we have discovered that picture noise scales roughly in accordance with t-1/2, that means automating this course of is viable and positively price doing for giant pattern sizes (see picture under). With the script, one can spend 5 minutes of their time stacking a whole lot of pictures, making this means of unveiling faint plasma easy.

Scatter plot shows the RMS of intensity decreases as the number of coadded images increases from 2 to 37 images.
Plot of how RMS (a quantitative measure of noise) varies with the variety of stacked pictures, stacked so as of accelerating RMS. As proven, assuming related integration occasions for every enter picture, RMS decreases roughly in accordance with t-1/2.

Astrobite edited by: Haley Wahl

Featured picture credit score: Omar French

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UR: Creating deeper VLA images by stacking

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