Multi-Instrument Solar Flare Observations II: A SC24 retrospective
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Contents |
Introduction
Using the search capabilities outlines in a previous nugget, we can now do a retrospective analysis to see how effective our coordinated observations - either planned or serendipitous - have been during Solar Cycle 24. We consider the first 6.5 years after SDO was launched (1 May 2010-31 Oct 2016), which encompasses the peak of Solar Cycle 24 (vertical dotted lines in Figure 1).
Statistics
First we shall take a look at how instrument performed individually. Table 1 shows the breakdown of flares listed in the SSW Latest Events catalog (by class) observed by each instrument. Note that MEGS-A had a 100% duty cycle up until 26 May 2014 when it suffered a power anomaly. Similarly, IRIS was only launched on 27 June 2013 so only flares after this date (in parentheses) were considered.
Instrument/Database | C-class | M-class | X-class | Total | Success Rate |
SSW Latest Events | 6,339 | 581 | 33 | 6,953 | N/A |
---|---|---|---|---|---|
RHESSI | 3,673 | 370 | 23 | 4,066 | 58% |
SDO/EVE MEGS-A | 3,825 | 343 | 19 | 4,187 | 100% |
SDO/EVE MEGS-B | 787 | 97 | 8 | 892 | 12% |
Hinode/EIS | 496 | 54 | 6 | 556 | 8% |
Hinode/SOT | 1,167 | 177 | 15 | 1,359 | 20% |
Hinode/XRT | 3,793 | 357 | 26 | 4,122 | 59% |
IRIS | 523 (3,349) | 76 (335) | 5 (16) | 604 (3,700) | 16% |
Now we look at how many flares (of all classes) were observed by various combinations (degree) of instruments. Again note that all 7 instruments were only operational together for 11 months, and the number of flares duration this time are given in parentheses.
Degree | Number of flares observed | % of potentially observable flares |
No instrument | 127 | 1.8% |
Only 1 instrument | 1,432 | 20.6% |
2 instruments | 2,371 | 34.1% |
3 instruments | 2,035 | 29.2 |
4 instruments | 720 | 10.3% |
5 instruments | 228 | 3.3% |
6 instruments | 37 | 0.5% |
All 7 instruments | 3 (934) | 0.3% |
UpSetR plots
To help visualise these relationships we have used UpSet, a novel tool for visualising intersecting datasets. This type of plot enables the efficient visualization the common elements of a large number of sets (the more common and familiar Venn diagram approach produces ineffective visualizations).
Conclusions
Biographical Note
Ryan Milligan is currently an Ernest Rutherford Fellow at the University of Glasgow.