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Solar Physics

, 293:48 | Cite as

Flare Prediction Using Photospheric and Coronal Image Data

  • Eric Jonas
  • Monica Bobra
  • Vaishaal Shankar
  • J. Todd Hoeksema
  • Benjamin Recht
Article

Abstract

The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar-image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that i) automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and May 2014, ii) combines these features with other features based on flaring history and a physical understanding of putative flaring processes, and iii) classifies these features to predict whether a solar active region will flare within a time period of \(T\) hours, where \(T = 2 \mbox{ and }24\). Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We find that when optimizing for the True Skill Score (TSS), photospheric vector-magnetic-field data combined with flaring history yields the best performance, and when optimizing for the area under the precision–recall curve, all of the data are helpful. Our model performance yields a TSS of \(0.84 \pm0.03\) and \(0.81 \pm0.03\) in the \(T = 2\)- and 24-hour cases, respectively, and a value of \(0.13 \pm0.07\) and \(0.43 \pm0.08\) for the area under the precision–recall curve in the \(T=2\)- and 24-hour cases, respectively. These relatively high scores are competitive with previous attempts at solar prediction, but our different methodology and extreme care in task design and experimental setup provide an independent confirmation of these results. Given the similar values of algorithm performance across various types of models reported in the literature, we conclude that we can expect a certain baseline predictive capacity using these data. We believe that this is the first attempt to predict solar flares using photospheric vector-magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona, and it points the way towards greater data integration across diverse sources in future work.

Notes

Acknowledgements

The data used here are courtesy of the GOES team and the Helioseismic and Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) science teams of the NASA Solar Dynamics Observatory. This work was supported by NASA Grant NAS5-02139 (HMI), and in part by DHS Award HSHQDC-16-3-00083, NSF CISE Expeditions Award CCF-1139158, DOE Award SN10040 DE-SC0012463, and DARPA XData Award FA8750-12-2-0331, and gifts from Amazon Web Services, Google, IBM, SAP, The Thomas and Stacey Siebel Foundation, Apple Inc., Arimo, Blue Goji, Bosch, Cisco, Cray, Cloudera, Ericsson, Facebook, Fujitsu, HP, Huawei, Intel, Microsoft, Mitre, Pivotal, Samsung, Schlumberger, Splunk, State Farm and VMware. B. Recht is supported by NSF award CCF-1359814, ONR awards N00014-14-1-0024 and N00014-17-1-2191, the DARPA Fundamental Limits of Learning (Fun LoL) Program, a Sloan Research Fellowship, and a Google Faculty Award.

Disclosure of Potential Conflicts of Interest

The authors declare they have no conflicts of interest.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of CaliforniaBerkeleyUSA
  2. 2.W.W. Hansen Experimental Physics LaboratoryStanford UniversityStanfordUSA

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