RFI Detection with Spiking Neural Networks

Radio noise coming from artificial technology on Earth and, increasingly, in space, is inevitable, interferes with
radio observations, and is difficult to detect and correct. Detecting and mitigating Radio Frequency Interference
(RFI) is key to maximizing the scientific output of radio telescopes. RFI detection is a difficult observation task that
requires an agent to distinguish genuine transient observations from radio interference. Hand-crafted algorithms
suffice for current instruments, but as telescopes grow in their sensitivity so too grows their requirement to filter
RFI. Ideal systems would be able to learn to detect novel types of RFI and flag otherwise anomolous observations.
While machine learning techniques have shown promise in this task [1]–[4], the inability to conduct online learning
efficiently and the associated energy cost in re-training models hampers using these techniques in practice, driving
investigation into more efficient methods [5], [6].

Spiking neural networks (SNNs) borrow more heavily from biological inspiration than Artificial Neural Networks
(ANNs) and are well suited to time-varying data processing. While SNNs are more dynamic than equivalently
sized ANNs, they are more difficult to train and difficult to simulate. Nascent neuromorphic hardware [7] imple-
ment SNNs directly, enabling enormous energy savings over traditional machine learning techniques if leveraged
correctly. This work reports on an initial investigation into encoding complex visibility data into SNNs and out-
line avenues to test the efficacy of SNNs against traditional machine learning methods. SNNs and neuromorphic
computing promise vast energy savings and better integration of continual learning; we outline how RFI detection
in radio astronomy is an excellent candidate to test these claims.