A Survey of Computational Tools in Solar Physics |
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Monica Bobra Submitted: 2020-05-13 12:09
The SunPy Project developed a 13-question survey to understand the software and hardware usage of the solar physics community. Of the solar-physics community, 364 members across 35 countries responded to our survey. We found that 99±0.5% of respondents use software in their research and 66% use the Python scientific-software stack. Students are twice as likely as faculty, staff scientists, and researchers to use Python rather than Interactive Data Language (IDL). In this respect, the astrophysics and solar-physics communities differ widely: 78% of solar-physics faculty, staff scientists, and researchers in our sample uses IDL, compared with 44% of astrophysics faculty and scientists sampled by Momcheva and Tollerud (2015). 63±4% of respondents have not taken any computer-science courses at an undergraduate or graduate level. We also found that most respondents use consumer hardware to run software for solar-physics research. Although 82% of respondents work with data from space-based or ground-based missions, some of which (e.g. the Solar Dynamics Observatory and Daniel K. Inouye Solar Telescope) produce terabytes of data a day, 14% use a regional or national cluster, 5% use a commercial cloud provider, and 29% use exclusively a laptop or desktop. Finally, we found that 73±4% of respondents cite scientific software in their research, although only 42±3% do so routinely.
Authors: Monica G. Bobra, Stuart J. Mumford, Russell J. Hewett, Steven D. Christe, Kevin Reardon, Sabrina Savage, Jack Ireland, Tiago M. D. Pereira, Bin Chen, and David Pérez-Suárez
Projects: None
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Publication Status: Published in Solar Physics: Solar Physics, 295, 57 (2020)
Last Modified: 2020-05-14 08:56
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Predicting Coronal Mass Ejections Using Machine Learning Methods |
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Monica Bobra Submitted: 2016-03-23 12:26
Of all the activity observed on the Sun, two of the most energetic events are flares and Coronal Mass Ejections (CMEs). Usually, solar active regions that produce large flares will also produce a CME, but this is not always true (Yashiro et al., 2005). Despite advances in numerical modeling, it is still unclear which circumstances will produce a CME (Webb & Howard, 2012). Therefore, it is worthwhile to empirically determine which features distinguish flares associated with CMEs from flares that are not. At this time, no extensive study has used physically meaningful features of active regions to distinguish between these two populations. As such, we attempt to do so by using features derived from [1] photospheric vector magnetic field data taken by the Solar Dynamics Observatory's Helioseismic and Magnetic Imager instrument and [2] X-ray flux data from the Geostationary Operational Environmental Satellite's X-ray Flux instrument. We build a catalog of active regions that either produced both a flare and a CME (the positive class) or simply a flare (the negative class). We then use machine-learning algorithms to [1] determine which features distinguish these two populations, and [2] forecast whether an active region that produces an M- or X-class flare will also produce a CME. We compute the True Skill Statistic, a forecast verification metric, and find that it is a relatively high value of approximately 0.8 plus or minus 0.2. We conclude that a combination of six parameters, which are all intensive in nature, will capture most of the relevant information contained in the photospheric magnetic field.
Authors: Monica G. Bobra and Stathis Ilondis
Projects: GOES X-rays ,SDO-HMI,STEREO
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Publication Status: Accepted for publication in the Astrophysical Journal
Last Modified: 2016-03-30 20:57
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