论文标题
使用机器学习技术从光谱成像观察中准确限制速度信息
Accurately constraining velocity information from spectral imaging observations using machine learning techniques
论文作者
论文摘要
确定光谱测量的准确等离子体多普勒（视线）速度是一项具有挑战性的努力，尤其是当弱色球吸收系线通常迅速发展，因此，其组成线轮廓中包含多个光谱成分时。在这里，我们提出了一种新的方法，该方法采用机器学习技术来识别观察到的光谱线中存在的基本组件，然后随后通过单个或多个voigt拟合来限制构成谱。我们的方法允许将光谱中存在的活性和静止成分鉴定并分离以进行后续研究。最后，我们采用CA II8542Å光谱成像数据集作为概念验证研究，以根据我们的代码来提取通常存在于Sunspot Chomophosperes中的两组大气剖面的适用性。采用最小化测试来验证结果的可靠性，在观测到的蒙布拉尔线概况之间，实现中位数降低了$χ^2 $值等于1.03。
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.
