基于无监督域自适应的恒星光谱解译
Interpreting Stellar Spectra with Unsupervised Domain Adaptation
摘要：
我们讨论了如何实现映射从大型集的不完美的模拟和观测数据与无监督域自适应。在假设模拟和观察到的数据分布共享一个共同的基本表示，我们展示了如何有可能在模拟和观察域之间转移。驱动的应用程序来解释恒星光谱巡天，我们构建了域传输管道从两个敌对的自动编码器在每个域上的解纠缠的潜在空间，和一个周期一致性约束。然后，我们构建了一个可微管道从物理恒星参数到现实的观测光谱，辅助补充生成代理物理仿真器网络。我们进一步验证了该方法在重建光谱质量和发现与元素丰度相关的新光谱特征方面的潜力。
英文摘要：
We discuss how to achieve mapping from large sets of imperfect simulations and observational data with unsupervised domain adaptation. Under the hypothesis that simulated and observed data distributions share a common underlying representation, we show how it is possible to transfer between simulated and observed domains. Driven by an application to interpret stellar spectroscopic sky surveys, we construct the domain transfer pipeline from two adversarial autoencoders on each domains with a disentangling latent space, and a cycle-consistency constraint. We then construct a differentiable pipeline from physical stellar parameters to realistic observed spectra, aided by a supplementary generative surrogate physics emulator network. We further exemplify the potential of the method on the reconstructed spectra quality and to discover new spectral features associated to elemental abundances.
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