使用机器学习技术精确约束来自光谱成像观测的速度信息
Accurately constraining velocity information from spectral imaging observations using machine learning techniques
摘要：
从光谱测量中确定准确的等离子体多普勒（视线）速度是一项具有挑战性的工作，特别是当弱色球吸收线往往迅速演变，因此，在其组成线剖面中包含多个光谱分量时。在这里，我们提出了一种新的方法，采用机器学习技术来识别观察到的谱线内存在的潜在成分，然后通过单个或多个Voigt拟合约束成分分布。我们的方法允许活性和静态成分存在于光谱中进行识别和分离，用于后续研究。最后，我们采用了钙II 8542光谱成像数据集作为概念验证研究基准的适用性，我们的代码提取两个组件的大气剖面，通常存在于太阳黑子色球。采用最小化检验来验证结果的可靠性，在观察到的和合成的本影线轮廓之间实现中值减少的$χ^2$值等于1.03。
英文摘要：
Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca II 8542 Å spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimisation tests are employed to validate the reliability of the results, achieving median reduced $χ^2$ values equal to 1.03 between the observed and synthesised umbral line profiles.
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