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A statistical study of the inferred transverse density profile of coronal loop threads observed with SDO/AIA

机译:用SDO / AIA观察到的推断冠状环线横向密度分布的统计研究

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Aims. We carry out a statistical study of the inferred coronal loop cross-sectional density profiles using extreme ultraviolet (EUV) imaging data from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO).udMethods. We analysed 233 coronal loops observed during 2015/2016. We consider three models for the density profile; the step function (model S ), the linear transition region profile (model L), and a Gaussian profile (model G). Bayesian inference is used to compare the three corresponding forward modelled intensity profiles for each loop. These are constructed by integrating the square of the density from a cylindrical loop cross section along the line of sight, assuming an isothermal cross section, and applying the instrumental point spread function.udResults. Calculating the Bayes factors for comparisons between the models, it was found that in 47 % of cases there is very strong evidence for model L over model S and in 45 % of cases very strong evidence for model G over S . Using multiple permutations of the Bayes factor the favoured density profile for each loop was determined for multiple evidence thresholds. There were a similar number of cases where model L or G are favoured, showing evidence for inhomogeneous layers and constantly varying density cross sections, subject to our assumptions and simplifications.udConclusions. For sufficiently well resolved loop threads with no visible substructure it has been shown that using Bayesian inference and the observed intensity profile we can distinguish between the proposed density profiles at a given AIA wavelength and spatial resolution. We have found very strong evidence for inhomogeneous layers, with model L being the most general, and a tendency towards thicker or even continuous layers.
机译:目的我们使用太阳动力学天文台(SDO)上大气成像组件(AIA)的极紫外(EUV)成像数据对推断的冠状环剖面密度分布图进行了统计研究。 udMethods。我们分析了2015/2016年观察到的233个冠状环。我们考虑密度分布的三个模型。阶跃函数(模型S),线性过渡区域轮廓(模型L)和高斯轮廓(模型G)。贝叶斯推断用于比较每个回路的三个相应的正向建模强度分布。这些是通过将沿着视线的圆柱环形横截面的密度平方平方(假定为等温横截面)并应用仪器点扩散函数而构造的。 udResults。计算贝叶斯因子以在模型之间进行比较,发现在47%的案例中,有非常有力的证据证明L模型优于S模型,在45%的案例中有非常有力的证据证明G模型优于S模型。使用贝叶斯因子的多个排列,针对多个证据阈值确定每个循环的最佳密度分布。在类似的情况下,倾向于使用模型L或G,这表明层不均匀且密度横截面不断变化的证据,但要经过我们的假设和简化。 ud结论。对于没有可见的子结构的,解析度足够好的环线,已经表明,使用贝叶斯推断和观察到的强度分布,我们可以在给定的AIA波长和空间分辨率下区分建议的密度分布。我们发现了非均匀层的非常有力的证据,其中模型L是最通用的,并且倾向于更厚甚至连续的层。

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