Fig. 3: Impact of Divisive Normalisation on Radio Frequency Inference (RFI) Detection in HERA Spectrograms.
From: Spiking neural networks for radio frequency interference detection in radio astronomy
Panels depict: a original spectrogram; b spectrogram after divisive normalisation, with background gradients reduced; c latency-based inference on the original spectrogram; d latency-based inference on the normalised spectrogram showing clearer RFI features; e residual between original inference and mask; f residual between normalised inference and mask, and g ground-truth RFI mask. Divisive normalisation significantly reduces background noise while preserving key RFI features, improving inference performance of the SNN.
