Deep Learning for Identification and Characterization of Ca ii Absorption Lines: A Multitask Convolutional Neural Network Approach

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成果类型：

期刊论文

作者：

Liu, Yang;Li, Jie;Gao, Linqing;Zhang, Haotong;Xu, Zhenghua;...

通讯作者：

Xu, ZH

作者机构：

[Lin, Wenbin; Liu, Yang] Univ South China, Sch Comp Sci, Hengyang 421001, Peoples R China.

[Lin, Wenbin; Xu, Zhenghua; Li, Jie; Gao, Linqing] Univ South China, Sch Math & Phys, Hengyang 421001, Peoples R China.

[Zhang, Haotong] Chinese Acad Sci, Natl Astron Observ, Beijing 100101, Peoples R China.

[Wang, Yu] Sapienza Univ Roma, ICRA & Dipartimento Fis, Piazzale Aldo Moro 5, I-00185 Rome, Italy.

[Lin, Wenbin; Wang, Yu] ICRANet, Pzza Repubbl 10, I-65122 Pescara, Italy.

通讯机构：

[Xu, ZH ] U

Univ South China, Sch Math & Phys, Hengyang 421001, Peoples R China.

语种：

英文

期刊：

ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES

ISSN：

0067-0049

年：

2025

卷：

276

期：

页码：

DOI：

10.3847/1538-4365/ad9250

基金类别：

MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809 [11973025]; National Natural Science Foundation of China

机构署名：

本校为第一且通讯机构

院系归属：

数理学院

摘要：

Quasar absorption lines are a powerful tool for studying the Universe, enabling us to probe distant gas, dust, and galaxy formation and evolution. However, detecting these lines, particularly Ca ii absorption lines, is a time-consuming and laborious process. Existing deep learning methods are prone to false positives and still require extensive manual verification and parameter measurement. This work presents three multitask convolutional neural network models and identifies the ResNet-CBAM model, which incorporates residual learning and an attention mechanism as the most effective. The result...

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Deep Learning for Identification and Characterization of Ca ii Absorption Lines: A Multitask Convolutional Neural Network Approach

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