Speaker
Description
Under the assumption that other advanced extraterrestrial civilisations exist - or have existed - elsewhere in our and other galaxies, the Breakthrough Listen Initiative has for the past decade taken the lead in the search for both direct and indirect technosignatures using a diversity of radio telescope facilities. Advances in instrumentation, computational processing power and most especially in ML have provided a means for researchers to both efficiently and rigorously search for anomalous signals against a continuing deluge of complex and very noisy observational radio data. One such Breakthrough Listen project, LOFTS (LOw Frequency pulsar, FRB, and Technosignature Survey) has been using two LOFAR (LOw Frequency ARray) outstations in Ireland and Sweden to simultaneously observe several galactic regions encompassing over a million nearby main sequence stars (mean distance of 1215 pc) at VHF radio frequencies. Being so geographically far apart, any locally bright sources of radio frequency interference can be identified and removed when the data from both stations are combined, with any remaining features common to both likely to be non-terrestrial in origin. I describe how we are developing a machine-learning framework that can expeditiously distinguish true dual-station coincidences from site-local RFI for broadened signal morphologies consistent from distant Doppler varying and locally stellar space weather distorted radio beacon signals. I place this work against the wider use of ML in the Breakthrough Listen Initiative to date, and the impact this methodology continues to make in resolving arguably one of the most profound questions for our species, whether we on our home planet are the sole sentient witnesses to our Universe.