An Alternative Method for Identifying Interplanetary Magnetic Cloud Regions

Spatio-temporal entropy (STE) analysis is used as an alternative mathematical tool to identify possible magnetic cloud (MC) candidates. We analyze Interplanetary Magnetic Field (IMF) data using a time interval of only 10 days. We select a convenient data interval of 2500 records moving forward by 20...

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Bibliographic Details
Published in:The Astrophysical journal 2017-03, Vol.837 (2), p.156
Main Authors: Ojeda-Gonzalez, A., Mendes, O., Calzadilla, A., Domingues, M. O., Prestes, A., Klausner, V.
Format: Article
Language:English
Subjects:
Astrophysics
ASTROPHYSICS, COSMOLOGY AND ASTRONOMY
DIAGNOSIS
EMISSION
ENTROPY
Extreme values
False alarms
Interplanetary magnetic field
INTERPLANETARY MAGNETIC FIELDS
Magnetic clouds
Magnetic fields
MASS
Mathematical analysis
methods: data analysis
SEASONAL THERMAL ENERGY STORAGE
SOLAR WIND
solar-terrestrial relations
STANDARDIZATION
SUN
Sun: coronal mass ejections (CMEs)
Sun: magnetic fields
Windows (intervals)
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Summary:Spatio-temporal entropy (STE) analysis is used as an alternative mathematical tool to identify possible magnetic cloud (MC) candidates. We analyze Interplanetary Magnetic Field (IMF) data using a time interval of only 10 days. We select a convenient data interval of 2500 records moving forward by 200 record steps until the end of the time series. For every data segment, the STE is calculated at each step. During an MC event, the STE reaches values close to zero. This extremely low value of STE is due to MC structure features. However, not all of the magnetic components in MCs have STE values close to zero at the same time. For this reason, we create a standardization index (the so-called Interplanetary Entropy, IE, index). This index is a worthwhile effort to develop new tools to help diagnose ICME structures. The IE was calculated using a time window of one year (1999), and it has a success rate of 70% over other identifiers of MCs. The unsuccessful cases (30%) are caused by small and weak MCs. The results show that the IE methodology identified 9 of 13 MCs, and emitted nine false alarm cases. In 1999, a total of 788 windows of 2500 values existed, meaning that the percentage of false alarms was 1.14%, which can be considered a good result. In addition, four time windows, each of 10 days, are studied, where the IE method was effective in finding MC candidates. As a novel result, two new MCs are identified in these time windows.
ISSN:0004-637X
1538-4357
DOI:10.3847/1538-4357/aa6034