Magnetic Feature Tracking in the SDO Era: Past Sacrifices, Recent Advances, and Future Possibilities
Wednesday, 17 December 2014: 4:03 PM
When implementing computer vision codes, a common reaction to the high angular resolution and the high cadence of SDO’s image products has been to reduce the resolution and cadence of the data so that it “looks like” SOHO data. This can be partially justified on physical grounds: if the phenomenon that a computer vision code is trying to detect was characterized in low-resolution, low cadence data, then the higher quality data may not be needed. But sacrificing at least two, and sometimes all four main advantages of SDO’s imaging data (the other two being a higher duty cycle and additional data products) threatens to also discard the perhaps more subtle discoveries waiting to be made: a classic baby-with-the-bath-water situation. In this presentation, we discuss some of the sacrifices made in implementing SWAMIS-EF, an automatic emerging magnetic flux region detection code for SDO/HMI, and how those sacrifices simultaneously simplified and complicated development of the code. SWAMIS-EF is a feature-finding code, and we will describe some situations and analyses in which a feature-finding code excels, and some in which a different type of algorithm may produce more favorable results. In particular, because the solar magnetic field is irreducibly complex at the currently observed spatial scales, searching for phenomena such as flux emergence using even semi-strict physical criteria often leads to large numbers of false or missed detections. This undesirable behavior can be mitigated by relaxing the imposed physical criteria, but here too there are tradeoffs: decreased numbers of missed detections may increase the number of false detections if the selection criteria are not both sensitive and specific to the searched-for phenomenon. Finally, we describe some recent steps we have taken to overcome these obstacles, by fully embracing the high resolution, high cadence SDO data, optimizing and partially parallelizing our existing code as a first step to allow fast magnetic feature tracking of full resolution HMI magnetograms. Even with the above caveats, if used correctly such a tool can provide a wealth of information on the positions, motions, and patterns of features, enabling large, cross-scale analyses that can answer important questions related to the solar dynamo and to coronal heating.