Open Collaboration on Breakthrough Listen Data from APF and GBT: Software Development and Data Analysis

To the point

Breakthrough Listen invites people to develop software and analysis methods for public APF and GBT data, noting APF data are usually easier to access than the large, complex GBT data, recommending Python (with Anaconda) and AstroPy for working with FITS spectra or voltage streams; example tasks include displaying the APF spectrum of Tabby’s Star, extracting a 1D spectrum, Gaussian-fitting the H-alpha line, and removing cosmic rays with results shared on Twitter using #BreakthroughAPFData, while GBT work such as reproducing the Voyager waterfall plot is more challenging; all code and docs live on the public GitHub repo, with credits to Chris Schodt, Steve Croft, Matt Lebofsky, and Nathaniel Adams.

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Open Collaboration on Breakthrough Listen Data from APF and GBT: Software Development and Data Analysis

Breakthrough Listen invites public participation to develop software and algorithms for APF and GBT data processing, noting that GBT data are large and complex while APF data are more accessible, encouraging newcomers to start with APF or with GBT if they have big‑data, ML, radio communications, or signal processing experience, emphasizing Python/Anaconda and AstroPy with FITS formats, pointing to the public repository at https://github.com/UCBerkeleySETI/breakthrough for tutorials including displaying the Tabby’s Star spectrum, extracting a 1D spectrum, reproducing the 1D plot, performing a Gaussian fit to the H-alpha line, estimating and removing cosmic rays, and sharing results on Twitter with #BreakthroughAPFData, noting that GBT tasks are more challenging (Voyager waterfall plot) though documentation and sample data exist in the repo, mentioning legacy SETI@home code in C with a porting page and upcoming open‑sourced Breakthrough Listen code, providing data access and hardware descriptions, and crediting Chris Schodt, Steve Croft, Matt Lebofsky, and Nathaniel Adams.