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Fluxed Flares: Data Mining Solar X-Ray Flares Triggered by Emerging Magnetic Flux

Loftus, Kaitlyn

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  <identifier identifierType="DOI">10.5281/zenodo.164872</identifier>
      <creatorName>Loftus, Kaitlyn</creatorName>
      <affiliation>Columbia University</affiliation>
    <title>Fluxed Flares: Data Mining Solar X-Ray Flares Triggered by Emerging Magnetic Flux</title>
    <subject>Solar activity</subject>
    <contributor contributorType="Supervisor">
      <contributorName>Schanche, Nicole</contributorName>
      <affiliation>Harvard-Smithsonian Center for Astrophysics</affiliation>
    <contributor contributorType="Supervisor">
      <contributorName>Saar, Steve</contributorName>
      <affiliation>Harvard-Smithsonian Center for Astrophysics</affiliation>
    <date dateType="Issued">2016-11-04</date>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
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    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;We investigate the association between emerging flux and solar X-ray flares to identify, and if possible quantify, distinguishing physical properties of flares triggered by flux emergence versus those triggered by other sources. Our study uses as its basis GOES-classified solar flares from March 2011 through June 2016 that have been identified by the Space Weather Prediction Center’s flare detection algorithm. The basic X-ray flare data is then enriched with data about related EUV-spectrum flares, emerging fluxes, active regions, eruptions, and sigmoids, which are all characterized by event-specific keywords, identified via SDO feature finding tools, and archived in the Heliophysics Events Knowledgebase (HEK). Using appropriate spatial and temporal parameters for each event type to determine association, we create a catalogue of solar events associated with each GOES-classified flare.  After accounting for the primitive state of many of these event detection algorithms, we statistically analyze the compiled dataset to determine the effects of an emerging flux trigger on flare properties. A two-sample Kolmogorov–Smirnov test confirms with 99.9% confidence that flares triggered by emerging flux have a different peak flux distribution than non-emerging-flux associated flares. We fit (with machine learning algorithms) flare peak flux as a function of emerging flux properties to better than 5.3% accuracy, while individual emerging flux properties show no linear or logarithmic correlation with peak flux. Our results will be of interest for predicting flare behavior as a function of magnetic activity (where we can use enhanced rates of emerging flux as a proxy for heightened stellar magnetic activity).&lt;br&gt;
Keywords: Solar flares, X-ray flares, Solar magnetic fields&lt;/p&gt;

&lt;p&gt; &lt;/p&gt;</description>
    <description descriptionType="Other">This work is supported under the NSF-REU solar physics program at SAO, grant number AGS-1560313.</description>
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