I am currently an Associate Research Professor in the Department of Physics at The George Washington University. My research focus is magnetic cataclysmic variables with an emphasis on their radio emission. I have over forty years of scientific programming experience.Profile Picture – adass-xxxi-2021/question_uploads/Kauai_ULkO2jo.png Affiliation –
The George Washington UniversityPosition –
Associate Research Professor
Twenty-five years ago at the ADASS VI meeting in Charlottesville, VA, a BoF entitled "Interactive Data Analysis Environments" set in motion the development and use of Python in Astronomy. Since then, many new interpreted, or interactive, programming languages have come on the scene. One of those languages is Julia, which was designed for high performance scientific computing. It can be considered the successor to Scientific Python. This BoF is about Julia in Astronomy and has the following goals: 1) to give a brief overview of the language's features and performance, 2) to summarize the current state of the JuliaAstro packages, and 3) to identify what JuliaAstro packages should be developed in the future, both in the near and long term.
An alternative to using the CLEAN algorithm to measure the flux density of sources in radio interferometry images is to fit the visibility (or UV) data directly using a parametrized model, such as a
delta or gaussian function. Several modules or packages currently exist for doing this fitting. However, each package has its limitations, e.g., a limited number of shapes, a limited number of
sources, a single Stokes parameter per source, or no frequency dependence. Because of these various limitations, a new package was developed called Visfit using the Julia programming language. Visfit
is fast (\~10 seconds per solution), flexible (no model limitations), and sensitive (~20% more than CLEAN). Part of its flexibility is due to Julia's piping operator which decouples the source function from
the Stokes parameters. Visfit is currently limited to reading Measurement Set (MS) data using the CASA Tables package.