Laidlaw Proposal

Abstract: The goal for The Search for Extra-terrestrial Intelligence (SETI) is to find evidence of technological signals beyond Earth. Radiofrequency SETI searches are often conducted in environments characterized by the high volume of interference and a vast quantity of unlabeled data. The main problem in Radio SETI is developing a generalizable technique in rejecting human radio frequency interference (RFI) and help narrow the searches for technosignatures. In this research project, we present a β−Convolutional Variational Autoencoder with an embedded discriminator combined with spectral clustering to help classify between RFI and SETI candidates in a semi-unsupervised fashion. We develop and evaluate the performance of this algorithm on a test bench of synthetic SETI events created with the data collected from the Breakthrough Listen project at the GreenBank telescope. Finally, this approach is being executed on the real GBT L-band dataset of over 1327 observational targets

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Peter Ma

Research Assistant, University of Toronto | UC Berkeley

Hey there! 👋 I'm a first-year student at the University of Toronto who's into machine learning, astronomy, and research. Currently, I'm an intern researcher at the UC Berkeley SETI Research Center to build a high-performance detection algorithm to search the nearest 1 million stars for signs of life beyond Earth using the VLA telescope and the MeerKAT telescope in South Africa. When I'm not looking for aliens I love rock climbing, painting, and enjoying the outdoors!