Automated Identification and Segmentation of Hi Sources in CRAFTS Using Deep Learning Method

• URL: http://arxiv.org/abs/2403.19912v2
• Date: Thu, 21 Nov 2024 07:08:47 GMT
• Title: Automated Identification and Segmentation of Hi Sources in CRAFTS Using Deep Learning Method
• Authors: Zihao Song, Huaxi Chen, Donghui Quan, Di Li, Yinghui Zheng, Shulei Ni, Yunchuan Chen, Yun Zheng,
• Abstract summary: Identifying neutral hydrogen (hi) galaxies from observational data is a significant challenge in hi galaxy surveys. We present a machine learning-based method for extracting hi sources from the three-dimensional (3D) spectral data obtained from the Commensal Radio Astronomy FAST Survey (CRAFTS) Our model, Unet-LK, utilizes the advanced 3D-Unet segmentation architecture and employs an elongated convolution kernel to effectively capture the intricate structures of hi sources.
• Score: 6.842583606693629
• License:
• Abstract: Identifying neutral hydrogen (\hi) galaxies from observational data is a significant challenge in \hi\ galaxy surveys. With the advancement of observational technology, especially with the advent of large-scale telescope projects such as FAST and SKA, the significant increase in data volume presents new challenges for the efficiency and accuracy of data processing.To address this challenge, in this study, we present a machine learning-based method for extracting \hi\ sources from the three-dimensional (3D) spectral data obtained from the Commensal Radio Astronomy FAST Survey (CRAFTS). We have carefully assembled a specialized dataset, HISF, rich in \hi\ sources, specifically designed to enhance the detection process. Our model, Unet-LK, utilizes the advanced 3D-Unet segmentation architecture and employs an elongated convolution kernel to effectively capture the intricate structures of \hi\ sources. This strategy ensures a reliable identification and segmentation of \hi\ sources, achieving notable performance metrics with a recall rate of 91.6\% and an accuracy of 95.7\%. These results substantiate the robustness of our dataset and the effectiveness of our proposed network architecture in the precise identification of \hi\ sources. Our code and dataset is publicly available at \url{https://github.com/fishszh/HISF}.

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