I’m the Innovation Scientist of the Center for Astrophysics and the American Astronomical Society. I help other astronomers do research better and faster. You can find out more on my About Me page, and you can contact me on Twitter as @pkgw or at email@example.com.
2019 May 9
It’s a truism in science that when you set out to solve big, inspiring questions you will soon find yourself mired in the technical details of some preposterously obscure topic. That’s surely the case for my work — while the main thrust of my astrophysics research is to study the magnetic fields of other stars and planets, one of the things I’ve been trying to think about a lot lately is the efficient computation of numerical parameters for polarized synchrotron radiative transfer. So it goes.
One of the tradeoffs of my new job is that I have a lot less time for doing astrophysics research myself, so this work is moving forward slowly these days. But I did manage to make something neat last year, and now I have time to write about it! It’s neurosynchro, an open-source Python package for training and using neural networks to compute approximate synchrotron radiative transfer coefficients. You can jump right into a tutorial, installation instructions, and the GitHub repository.