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 ɪɪ 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. Minimization tests are employed to validate the reliability of the results, achieving median reduced χ2-values equal to 1.03 between the observed and synthesized umbral line profiles. This article is part of the Theo Murphy meeting issue ‘High-resolution wave dynamics in the lower solar atmosphere’.

中文翻译：

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使用机器学习技术准确约束光谱成像观测中的速度信息


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经常快速演变并因此在其组成线轮廓中包含多个光谱成分时。在这里，我们提出了一种新颖的方法，该方法采用机器学习技术来识别观察到的谱线中存在的基础成分，然后通过单个或多个 Voigt 拟合来约束成分轮廓。我们的方法可以识别和分离光谱中存在的活性和静态成分以供后续研究。最后，我们采用 Ca ɪɪ 8542 Å 光谱成像数据集作为概念验证研究，以对我们的代码提取太阳黑子色球中常见的二分量大气剖面的适用性进行基准测试。采用最小化测试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了等于 1.03 的中值减少 χ2 值。本文是 Theo Murphy 会议问题“太阳低层大气中的高分辨率波动力学”的一部分。