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arXiv 2103.05371astro-ph.SRastro-ph.IMstat.ML
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利用无监督机器学习探索日冕加热

Exploring Coronal Heating Using Unsupervised Machine-Learning

Shabbir Bawaji, Ujjaini Alam, Surajit Mondal, Divya Oberoi

AI总结:

针对日冕加热之谜,本文利用无监督机器学习技术表征宁静太阳低频射电脉冲发射的形态,在8000余幅图像中稳健识别出约34500个二维椭圆高斯特征。

摘要:

太阳可见圆盘温度仅为5800 K,而日冕温度却维持在约一百万K,这一令人费解的谜团一直是太阳物理学中的长期难题。Mondal(2020)近期的一项研究首次提供了证据,表明宁静太阳区域存在大量普遍的低频射电脉冲发射,这可能成为解开这一谜团的关键。这些特征的出现率约为每分钟五百次,其强度仅占背景稳态发射的百分之几。探索这一日冕加热问题解决方案可行性的后续步骤之一,是理解这些发射的形态。为实现这一目标,我们开发了一种基于无监督机器学习方法的技术,用于表征这些脉冲发射的形态。在此,我们展示了将该技术应用于超过8000幅图像(涵盖70分钟数据)的结果,其中约34,500个特征能够被稳健地表征为二维椭圆高斯。

英文摘要:

The perplexing mystery of what maintains the solar coronal temperature at about a million K, while the visible disc of the Sun is only at 5800 K, has been a long standing problem in solar physics. A recent study by Mondal(2020) has provided the first evidence for the presence of numerous ubiquitous impulsive emissions at low radio frequencies from the quiet sun regions, which could hold the key to solving this mystery. These features occur at rates of about five hundred events per minute, and their strength is only a few percent of the background steady emission. One of the next steps for exploring the feasibility of this resolution to the coronal heating problem is to understand the morphology of these emissions. To meet this objective we have developed a technique based on an unsupervised machine learning approach for characterising the morphology of these impulsive emissions. Here we present the results of application of this technique to over 8000 images spanning 70 minutes of data in which about 34,500 features could robustly be characterised as 2D elliptical Gaussians.

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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