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Workshop: Machine Learning and the Physical Sciences

Detecting structured signals in radio telescope data using RKHS

Russell Tsuchida · Suk Yee Yong


Fast Radio Bursts (FRBs) are rare high-energy pulses detectable by radio telescopes whose physical description is currently unknown. Due to the volume of data produced by radio telescopes, efficient computational methods for automatically detecting FRBs and other signals of interest are required. The most basic of these methods involves fitting a physical model of frequency despersion to the observed signal, and flagging a detection if the dedispersed signal has high power. This method can successfully detect simple pulses, but can fail to detect other interesting astronomical signals. We propose a method for dedispersion that does not use a physical model but instead uses a flexible element of a reproducing kernel Hilbert space (RKHS). Our method can outperform classical dedispersion on a benchmark of real and synthetic data consisting of FRBs and non-physical signals.

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