论文标题
深层太阳能Alma神经网络估计器，用于图像改进和小型动力学的估计值
Deep solar ALMA neural network estimator for image refinement and estimates of small-scale dynamics
论文作者
论文摘要
随着角度分辨率降低，小规模特征的可观察到的特征的对比和大小降低。在1.25毫米处的合成可观察图的高振动时间序列是由太阳气氛的3D磁性水力动力双纤维模拟产生的，并将其降解为与Atacama大毫米/亚米计阵列（Alma）的观测数据相对应的角度分辨率（Alma）。深太阳能Alma神经网络估计器（Deep-Sanne）是一种人工神经网络，旨在改善太阳观测的分辨率和对比度。这是通过识别与观测数据相对应的角度分辨率的小规模特征的空间和时间域中的动态模式来完成的，并将它们与来自磁流失动力学模拟的高度分辨的非结构化数据相关联。第二个模拟用于验证性能。 Deep-Sanne提供了亮度温度的估计降解的地图，该地图可用于过滤，以滤过最可能显示出高准确性和校正因子的位置，以构建优化图像，这些图像比观察分辨率相比显示出更高的对比度和更准确的亮度温度。 Deep-Sanne揭示了更多的小规模特征，并估计了相对于高度分辨率的数据，平均准确性为94.0％，而观察分辨率为43.7％。通过使用时间域的其他信息，Deep-Sanne可以比标准的二维Deonvolver技术更好地恢复高对比度。 Deep-Sanne应用于观测太阳ALMA数据。深山的精制图像对于分析小规模和动态特征很有用。他们可以高准确地识别数据中的位置，以进行深入分析，并可以对太阳观测的更有意义地解释。
The contrasts and magnitude of observable signatures of small-scale features degrade as angular resolution decreases. High-cadence time-series of synthetic observable maps at 1.25 mm were produced from 3D magnetohydrodynamic Bifrost simulations of the solar atmosphere and degraded to the angular resolution corresponding to observational data with the Atacama Large Millimeter/sub-millimeter Array (ALMA). The Deep Solar ALMA Neural Network Estimator (Deep-SANNE) is an artificial neural network trained to improve the resolution and contrast of solar observations. This is done by recognizing dynamic patterns in both the spatial and temporal domains of small-scale features at an angular resolution corresponding to observational data and correlated them to highly resolved nondegraded data from the magnetohydrodynamic simulations. A second simulation, was used to validate the performance. Deep-SANNE provides maps of the estimated degradation of the brightness temperature, which can be used to filter for locations that most probably show a high accuracy and as correction factors in order to construct refined images that show higher contrast and more accurate brightness temperatures than at the observational resolution. Deep-SANNE reveals more small-scale features and estimates the excess temperature of brightening events with an average accuracy of 94.0% relative to the highly resolved data, compared to 43.7% at the observational resolution. By using the additional information of the temporal domain, Deep-SANNE can restore high contrasts better than a standard two-dimensional deconvolver technique. Deep-SANNE is applied on observational solar ALMA data. The Deep-SANNE refined images are useful for analysing small-scale and dynamic features. They can identify locations in the data with high accuracy for an in-depth analysis and allow a more meaningful interpretation of solar observations.
