Discipline: Computer Sciences and Information Management
Subcategory: Astronomy and Astrophysics
Room: Park Tower 8219
Christopher Murphy - University of the Virgin Islands
Co-Author(s): Steve Croft, Ryan Dana, and Dave DeBoer, University of California, Berkeley, Berkeley, California
Differentiating between signals from human technology (RFI) and signals emitted from a distant world is one of the most challenging tasks in radio SETI. We have tried to overcome this issue by placing telescopes in remote, protected areas such as Green Bank, WV. Nevertheless, we are unable to completely escape RFI. The myriad of operational satellites in Earths orbit all contribute as sources of RFI. Fortunately, the position of a satellite can be accurately predicted thanks to well characterized orbital parameters that are publicly available for most satellites. We found examples of satellites impacting Breakthrough Listen archival data taken using the Green Bank Telescope. Our main objective is to use these examples in order to identify characteristics in the data that coincide with particular satellites and then use this information to mitigate satellite RFI, and eventually provide labeled examples of RFI for input to machine learning algorithms.
Funder Acknowledgement(s): Berkeley SETI Research Center
Faculty Advisor: Steve Croft, email@example.com
Role: Develop python based code to extract metadata from Breakthrough Listen archival data, download satellite orbital information from Space-Track.org, use that information to compute the position of several satellites during the time of observation.