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arXiv 2103.07373astro-ph.SRastro-ph.IM
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太阳光谱观测的压缩:NASA的IRIS卫星观测的Mg II k谱线廓线的个例研究

Compression of Solar Spectroscopic Observations: a Case Study of Mg II k Spectral Line Profiles Observed by NASA's IRIS Satellite

Viacheslav M Sadykov, Irina N Kitiashvili, Alberto Sainz Dalda, Vincent Oria, Alexander G Kosovichev, Egor Illarionov

摘要:

在这项研究中,我们提取的深功能和调查的压缩Mg II K谱线的配置文件在安静的太阳地区所观察到的美国宇航局的IRIS卫星。用于分析的线轮廓数据集于2020年4月20日在太阳圆盘的中心获得,在数据清理后包含近30万个单独的Mg II k线轮廓。数据被分成训练和测试子集。该训练子集用于训练不同嵌入层尺寸的自动编码器。在测试子集上实施早期停止准则以防止模型过拟合。我们的研究结果表明,它是可能的,以压缩的谱线轮廓超过27倍(这对应于减少的数据维数从110到4),同时具有4 DN的平均重建误差,这是可比的线连续的变化。当嵌入层的维数从1增加到4时,偶数统计矩的均方误差和重建误差急剧减小,并且当维数更高时几乎停止减小。对于高于4的值,在训练中观察到的偶尔改进表明,如果使用其他训练策略和更长的训练时间,则可能获得更好的紧凑嵌入。关键四维情况下学习的功能可以解释。特别地,这四个特征中的三个分别主要控制线宽度、线不对称性和线倾角形成。所提出的结果是第一次尝试获得一个紧凑的嵌入光谱线轮廓,并确认这种方法的价值,特别是特征提取,数据压缩和去噪。

英文摘要:

In this study we extract the deep features and investigate the compression of the Mg II k spectral line profiles observed in quiet Sun regions by NASA's IRIS satellite. The data set of line profiles used for the analysis was obtained on April 20th, 2020, at the center of the solar disc, and contains almost 300,000 individual Mg II k line profiles after data cleaning. The data are separated into train and test subsets. The train subset was used to train the autoencoder of the varying embedding layer size. The early stopping criterion was implemented on the test subset to prevent the model from overfitting. Our results indicate that it is possible to compress the spectral line profiles more than 27 times (which corresponds to the reduction of the data dimensionality from 110 to 4) while having a 4 DN average reconstruction error, which is comparable to the variations in the line continuum. The mean squared error and the reconstruction error of even statistical moments sharply decrease when the dimensionality of the embedding layer increases from 1 to 4 and almost stop decreasing for higher numbers. The observed occasional improvements in training for values higher than 4 indicate that a better compact embedding may potentially be obtained if other training strategies and longer training times are used. The features learned for the critical four-dimensional case can be interpreted. In particular, three of these four features mainly control the line width, line asymmetry, and line dip formation respectively. The presented results are the first attempt to obtain a compact embedding for spectroscopic line profiles and confirm the value of this approach, in particular for feature extraction, data compression, and denoising.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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