用生成对抗网络分离恒星光谱中的物理与化学特征
A method based on Generative Adversarial Networks for disentangling physical and chemical properties of stars in astronomical spectra
- 设计对抗训练的编码解码架构，实现物理与化学属性解耦
- 在合成数据上验证，成功消除温度重力影响，仅保留化学成分差异
- 提供开源框架GANDALF，支持可复现与跨领域扩展
在大数据时代，数据压缩需兼顾信息保真。本文提出一种基于生成对抗网络的编码解码架构，用于天体光谱分析。目标是获得一个中间表示，使恒星光谱中表面温度和重力等主要物理属性的贡献消失，而方差仅反映化学组成的影响。通过深度学习在潜在空间中解耦所需参数。该方法采用按待解耦参数设置独立判别器的设计，避免了单个判别器因离散化导致的指数级组合问题。使用来自APOGEE和Gaia调查的合成天文数据进行测试。同时，本文发布开源解耦框架GANDALF，供社区复制、可视化及拓展至其他领域。
原文摘要 · Abstract (English)
Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed, where adversarial training, a modification of the traditional autoencoder, is used in the context of astrophysical spectral analysis. The goal of this proposal is to obtain an intermediate representation of the astronomical stellar spectra, in which the contribution to the flux of a star due to the most influential physical properties (its surface temperature and gravity) disappears and the variance reflects only the effect of the chemical composition over the spectrum. A scheme of deep learning is used with the aim of unraveling in the latent space the desired parameters of the rest of the information contained in the data. This work proposes a version of adversarial training that makes use of a discriminator per parameter to be disentangled, thus avoiding the exponential combination that occurs in the use of a single discriminator, as a result of the discretization of the values to be untangled. To test the effectiveness of the method, synthetic astronomical data are used from the APOGEE and Gaia surveys. In conjunction with the work presented, we also provide a disentangling framework (GANDALF) available to the community, which allows the replication, visualization, and extension of the method to domains of any nature.
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