Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables
Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables
复制标题
通过机器学习了解活动区域核心的加热。
DOI：
10.3847/1538-4357/ab290c
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
N. Viall
中科院分区：
文献类型：
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作者：
W. Barnes;Stephen J. Bradshaw;N. Viall
为了充分限制日冕活动区核心能量沉积的频率，需要对详细的模型和观测数据进行系统的比较。在本文中，我们描述了一个流水线的正演模拟活动区发射磁场外推和场对齐的流体动力学模型。我们使用这个管道来预测低，中，高频纳米片的有源区NOAA 1158随时间变化的发射。在我们预测的多波长，时间相关图像的每个像素中，我们计算两个常用的诊断：发射测量斜率和时间滞后。我们发现，加热频率的签名坚持在这两个诊断。特别是，我们的研究结果表明，排放测量斜率的分布变窄，平均值随加热频率的降低而降低，排放测量斜率的范围与过去的观测和模拟工作是一致的。此外，我们发现，随着加热频率的降低，时间滞后变得越来越空间相干，而整个活动区域的时间滞后的分布随着加热频率的增加变得更加广泛。在后续论文中，我们在这些预测诊断上训练了一个随机森林分类器，并使用该模型对NOAA 1158的真实的观测结果进行分类。
To adequately constrain the frequency of energy deposition in active region cores in the solar corona, systematic comparisons between detailed models and observational data are needed. In this paper, we describe a pipeline for forward modeling active region emission using magnetic field extrapolations and field-aligned hydrodynamic models. We use this pipeline to predict time-dependent emission from active region NOAA 1158 for low-, intermediate-, and high-frequency nanoflares. In each pixel of our predicted multi-wavelength, time-dependent images, we compute two commonly used diagnostics: the emission measure slope and the time lag. We find that signatures of the heating frequency persist in both of these diagnostics. In particular, our results show that the distribution of emission measure slopes narrows and the mean decreases with decreasing heating frequency and that the range of emission measure slopes is consistent with past observational and modeling work. Furthermore, we find that the time lag becomes increasingly spatially coherent with decreasing heating frequency while the distribution of time lags across the whole active region becomes more broad with increasing heating frequency. In a follow-up paper, we train a random forest classifier on these predicted diagnostics and use this model to classify real observations of NOAA 1158 in terms of the underlying heating frequency.