An investigation of the causal relationship between sunspot groups and coronal mass ejections by determining source active regions
An investigation of the causal relationship between sunspot groups and coronal mass ejections by determining source active regions
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通过确定源活动区域研究太阳黑子群与日冕物质抛射之间的因果关系
DOI：
10.1093/mnras/stab1816
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发表时间：
2021
影响因子：
4.8
通讯作者：
Wang, Jason T
中科院分区：
文献类型：
--
作者：
Raheem, Abd-ur;Cavus, Huseyin;Coban, Gani Caglar;Kinaci, Ahmet Cumhur;Wang, Haimin;Wang, Jason T
虽然在日冕物质抛射星表中已经确定了一些日冕物质抛射的源活动区，但绝大多数日冕物质抛射没有已确定的源活动区。我们提出了一种方法，使用过滤过程和机器学习来识别与很大一部分日冕物质抛射相关的太阳黑子群，并将这些识别出的太阳黑子群的物理参数与其对应的日冕物质抛射的性质进行比较，以找到日冕物质抛射启动背后的机制。这些CME摘自NASA网站上的协调数据分析研讨会(CDAW)数据库。日震和磁成像仪(HMI)活动区域斑块(HARP)取自斯坦福大学联合科学操作中心(JSOC)数据库。在定制过滤程序的帮助下，然后通过训练长期短期记忆网络(LSTM)以确定从矢量和视线磁图得出的物理磁参数中的模式，确定了日冕物质抛物源活动区。神经网络同时考虑这些磁参数的时间序列数据，并在日冕物质抛射开始时学习模式。这个神经网络随后被用来识别从2011年到2020年记录的CME的源竖琴。在上述期间，神经网络能够可靠地确定在CDAW数据库所列14个 604个中的4 895个CME的源HARP。
Although the source active regions of some coronal mass ejections (CMEs) were identified in CME catalogues, vast majority of CMEs do not have an identified source active region. We propose a method that uses a filtration process and machine learning to identify the sunspot groups associated with a large fraction of CMEs and compare the physical parameters of these identified sunspot groups with properties of their corresponding CMEs to find mechanisms behind the initiation of CMEs. These CMEs were taken from the Coordinated Data Analysis Workshops (CDAW) data base hosted at NASA’s website. The Helioseismic and Magnetic Imager (HMI) Active Region Patches (HARPs) were taken from the Stanford University’s Joint Science Operations Center (JSOC) data base. The source active regions of the CMEs were identified by the help of a custom filtration procedure and then by training a long short-term memory network (LSTM) to identify the patterns in the physical magnetic parameters derived from vector and line-of-sight magnetograms. The neural network simultaneously considers the time series data of these magnetic parameters at once and learns the patterns at the onset of CMEs. This neural network was then used to identify the source HARPs for the CMEs recorded from 2011 till 2020. The neural network was able to reliably identify source HARPs for 4895 CMEs out of 14 604 listed in the CDAW data base during the aforementioned period.
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影响因子：
20.9
作者：
S. Toriumi;Haimin Wang
通讯作者：
S. Toriumi;Haimin Wang
影响因子：
2.8
作者：
I. Richardson
通讯作者：
I. Richardson
DOI：
10.1088/1749-4699/8/1/014009
发表时间：
2015-05
期刊：
Computational Science & Discovery
影响因子：
--
作者：
The SunPy Community;S. Mumford;S. Christe;D. P'erez-Su'arez;J. Ireland;A. Shih;A. Inglis;Simon Liedtke;Russell J. Hewett;F. Mayer;Keith Hughitt;N. Freij;T. Mészáros;Samuel Bennett;Michael Malocha;John G Evans;Ankit Agrawal;Andrew Leonard;T. Robitaille;B. Mampaey;Jose Iván Campos Rozo;M. Kirk
通讯作者：
The SunPy Community;S. Mumford;S. Christe;D. P'erez-Su'arez;J. Ireland;A. Shih;A. Inglis;Simon Liedtke;Russell J. Hewett;F. Mayer;Keith Hughitt;N. Freij;T. Mészáros;Samuel Bennett;Michael Malocha;John G Evans;Ankit Agrawal;Andrew Leonard;T. Robitaille;B. Mampaey;Jose Iván Campos Rozo;M. Kirk
影响因子：
2.8
作者：
P. Manoharan
通讯作者：
P. Manoharan
影响因子：
3.9
作者：
Karim, Fazle;Majumdar, Somshubra;Darabi, Houshang
通讯作者：
Darabi, Houshang