Can I read A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm on EtoBox?

A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm by Ma, Peter Xiangyuan; Croft, Steve; Lintott, Chris; Siemion, Andrew P. V. is a scholarly article available to read on EtoBox.

What is A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm about?

Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more sophisticated RFI rejection algorithms. The "needle in a haystack" nature of searches for transients and technosignatures requires us to develop methods that can determine whether a signal of interest has unique properties, or is a part of some larger set of pernicious RFI. In the past, this vetting has required onerous manual inspection of very large numbers of signals. In this paper we present a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained a B-Variational Autoencoder on signals returned by an energy detection algorithm. We then adapted a positional embedding layer from classical Transformer architecture to a embed additional metadata, which we demonstrate using a frequency-based embedding. Next we used the encoder component of the B-Variational Autoencoder to extract features from small (~ 715,Hz, with a resolution of 2.79Hz per frequency bin) windows in the radio spectrogram. We used our algorithm to conduct a search for a given

Author

Ma, Peter Xiangyuan; Croft, Steve; Lintott, Chris; Siemion, Andrew P. V.

Published

2023

Language

EN