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

• DOI: 10.48550/arxiv.2403.19912
• Publication Date: 2024-01-01
• Authors: Zihao Song; Huaxi Chen; Donghui Quan; Di Li; Yinghui Zheng; Shulei Ni; Yunchuan Chen; Yun Zheng
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Astrophysics of Galaxies (astro-ph.GA); Computer Science - Computer Vision and Pattern Recognition; FOS: Physical sciences; Astrophysics - Instrumentation and Methods for Astrophysics; Astrophysics - Astrophysics of Galaxies; Instrumentation and Methods for Astrophysics (astro-ph.IM)

ABSTRACT Identifying neutral hydrogen (${\rm H}\, {\small I}$) galaxies from observational data is a significant challenge in ${\rm H}\, {\small I}$ 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 ${\rm H}\, {\small I}$ sources from the 3D spectral data obtained from the Commensal Radio Astronomy FAST Survey (CRAFTS). We have carefully assembled a specialized data set, HISF, rich in ${\rm H}\, {\small I}$ 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 ${\rm H}\, {\small I}$ sources. This strategy ensures a reliable identification and segmentation of ${\rm H}\, {\small I}$ sources, achieving notable performance metrics with a recall rate of 91.6 per cent and an accuracy of 95.7 per cent. These results substantiate the robustness of our data set and the effectiveness of our proposed network architecture in the precise identification of ${\rm H}\, {\small I}$ sources.