Vol 23, No 11

Detecting H i Galaxies with Deep Neural Networks in the Presence of Radio Frequency Interference

Ruxi Liang, Furen Deng, Zepei Yang, Chunming Li, Feiyu Zhao, Botao Yang, Shuanghao Shu, Wenxiu Yang, Shifan Zuo, Yichao Li et al.


In the neutral hydrogen (H i) galaxy survey, a significant challenge is to identify and extract the H igalaxy signal from the observational data contaminated by radio frequency interference (RFI). For a drift-scan survey, or more generally a survey of a spatially continuous region, in the time-ordered spectral data, the H i galaxies and RFI all appear as regions that extend an area in the time-frequency waterfall plot, so the extraction of the H i galaxies and RFI from such data can be regarded as an image segmentation problem, and machine-learning methods can be applied to solve such problems. In this study, we develop a method to effectively detect and extract signals of H i galaxies based on a Mask R-CNN network combined with the PointRend method. By simulating FAST-observed galaxy signals and potential RFI impact, we created a realistic data set for the training and testing of our neural network. We compared five different architectures and selected the best-performing one. This architecture successfully performs instance segmentation of H igalaxy signals in the RFI-contaminated time-ordered data, achieving a precision of 98.64% and a recall of 93.59%.


Key words: methods: data analysis – methods: observational – techniques: image processing

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