Neural-Based Compression for the Spectral Data of the New Vacuum Solar Telescope

IF 2.7 3区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Solar Physics Pub Date : 2025-03-07 DOI:10.1007/s11207-025-02447-7

Yan Dong, Zhenping Qiang, Jiayan Yang, Yunfang Cai, Qingyang Chen, Jia Cao

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引用次数: 0

Abstract

Due to the rapid increase in spectral data generation as well as storage and transmission constraints, data compression has become particularly important for the New Vacuum Solar Telescope (NVST) at Yunnan Observatory. In this paper, we present a method for compressing NVST Ca II (8542 Å) spectral data based on a Convolutional Variational Autoencoder (VAE). Our results show that the compression ratios of the VAE-based approach may achieve as high as 107, while keeping the error between the decompressed data and the original data within the inherent error range of the raw data. This is much better than the appropriate compression ratio of 30 that is attained using the current PCA-based approach. Furthermore, the stability of the VAE approach is demonstrated by the almost constant differences between the VAE-compressed data and the raw data when the compression ratio ranges from 8 to 107. We also investigated Doppler velocity images deduced from the VAE-compressed data and found that the error in Doppler velocity is significantly less than 5 km s⁻¹ when the compression ratio does not exceed 107.

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来源期刊

Solar Physics 地学天文-天文与天体物理

CiteScore

5.10

自引率

17.90%

发文量

146

审稿时长

1 months

期刊介绍： Solar Physics was founded in 1967 and is the principal journal for the publication of the results of fundamental research on the Sun. The journal treats all aspects of solar physics, ranging from the internal structure of the Sun and its evolution to the outer corona and solar wind in interplanetary space. Papers on solar-terrestrial physics and on stellar research are also published when their results have a direct bearing on our understanding of the Sun.