Tips for Machine Learning Prediction of Solar Flares

[bytewatchman]

Hi friends,

I've been lurking on this site for some time and just now decided to create an account. Essentially, I am working on a computer vision project to classify solar flares, however differing from existing approaches I plan on regressing on the X-ray flux from the sun rather than trying to classify whether there is a risk of solar flare. Not only does this approach not rely on human labeling, I am also hopeful that it may help us gain better insight on solar physics through its more granular predictions. (also motivated by the fact that I think existing classifiers, e.g. https://defn.nict.go.jp/index_eng.html, do not have particularly good performance)

I have a couple of questions for experienced flare-watchers and/or machine learning enthusiasts.

1. Are there any general tips/trends you would suggest thinking about for solar flare prediction?
2. Do you think such a prediction of X-ray flux from previous X-ray flux and a sequence of images of the sun would be feasible?
3. Currently we are doing short-term predictions of solar flares in the next few hours from a few solar images. Do you think longer-term predictions would be feasible? Would these potentially be higher-quality than short-term predictions (given same training data)
4. Since large spikes from solar flares are rare, do you have any tips on making it so the regression catches these rare events, rather than avoiding them?

Thanks in advance for your help: examples of the data we are using is attached if you would like to reference. Cheers!

[Philalethes]

Greetings. Machine learning for Solar flare predictions is always an interesting topic; I've considered trying to put something together myself, but it's definitely no small task, and trying to do it from just a couple of variables is likely to prove very difficult. I certainly hope your investigations will provide some useful insights, though. Here are some comments on the points you're asking about, based on what I've gathered after a few years of delving into space weather and Solar physics, and from a background of computer science myself:
 

1. I think one of the more important things one would have to do to increase chances of success in predicting flares would be to take more variables into account; as many as possible that you think might play a role, in fact. As you probably realize this can make models large and unruly, but I do believe this is the best bet, because not only are there many variables we already know are interesting when it comes to flare activity, but even some variables that might not immediately be obviously related to it could e.g. be correlated with unknown variables that you'd very much like to include in the model. Some examples would e.g. be using more specific magnetic field data from HMI to try and register how the magnetic fields are moving, including data from the coronagraphs somehow (e.g. using computer vision to register patterns in ejections and outflows), or incorporating Solar wind data (some patterns that are hard to detect could possibly signal an increased likelihood of flaring, even if we only get the Solar wind data a few days later and even if it's unlikely), and perhaps even feeding processed imagery of the visible disc at different wavelengths could be useful. Something like multivariate time-series forecasting using these and other variables, and features based on them, is what I envision would be a good approach, and letting the model figure out what seems to be most relevant.
    
2. Sorry to say, but I don't think that's really very feasible; I could definitely be wrong, though, and I'd be delighted to be proven wrong given how that would make forecasting flares a lot easier. The reason I don't think that would necessarily work very well is that flaring is typically driven by underlying processes, e.g. subsurface activity and movements of the magnetic field, and will often not give much of a warning at all in terms of flux until the flare occurs. It's certainly not always true, of course, sometimes regions will be flaring a bit on and off first, but then again this often leads to nothing as well. I guess you'll have to see if you can indeed pry any predictive patterns out of it. At least using imagery might give some more clue to the model in that regard, as it can at least register when rapid growth of spots is occurring, but I doubt just using the intensitygram (continuum) will be sufficient given the lack of distinction between polarities.
    
3. If you're just doing it over a few hours using the very latest imagery that does make it more feasible, but it's unclear to me to what extent you are able to encapsulate certain important factors from the imagery, like when rapid sunspot growth occurs in a concentrated area, and when the magnetic field of such rapidly growing regions seems conducive to flaring (e.g. the development of deltas). If you're able to capture some of that information then I would guess you could at the very least improve the chance of successful prediction, as the model should be able to notice relationships between such rapid growth and flares. For the most part I think long-term predictions would be more difficult, but there could be certain factors that lend themselves more to that; for example, not long ago we observed an anti-Hale region, and from reading about it it was apparently common for such regions to produce significant flares within the following days, which the region did indeed end up doing. As mentioned in the address to the first question I also think long-term predictions would benefit more from utilizing more variables that could be relevant.
    
4. Well, that's essentially the heart of the issue here, because regression will typically have a hard time accounting for such spikes, especially given that they're likely caused by variables not being included in the model at all. I think such a model as you're making would probably have better success predicting the general movement in the overall flux over time, but be less successful at predicting individual flares; again, would love to be proven wrong, so don't take this as discouragement at all.
    

Hope this might be of some help in thinking further about how to potentially predict flares. Here's the obligatory xkcd for good measure:

Edited June 6, 2023 by Philalethes
typo

[bytewatchman]

Do you know other data series that I could potentially include in prediction that would make such a task better (e.g. magnetograms?)

[Philalethes]

5 minutes ago, bytewatchman said:

Do you know other data series that I could potentially include in prediction that would make such a task better (e.g. magnetograms?)

Well, the problem with the short forecasting time is the lack of sufficiently live data, but including the magnetogram data somehow would probably be a good addition for the reasons stated above, and most of that data is updated very frequently. For longer-term forecasts is when many of the other series become possible to use, but of course the problem then would be the reduced accuracy of prediction; you can e.g. get an overview of some of the magnetogram data here and how often the respective series are updated.

Bottom line is that it's going to be very difficult to achieve anything accurate with a simple model, and I doubt that even including the other series I mentioned in the first reply would lead to much improvement, it would probably be rather marginal (at least that's what I suspect, but who knows what patterns might reveal something about the underlying activity that isn't readily apparent). I think your best bet would indeed be to see if you can include magnetogram data somehow, because to our best knowledge, at least to my understanding, it's the tangling of the magnetic field as it moves with the plasma that leads to the stored magnetic energy released during flares.

[bytewatchman]

Thanks so much! I'll keep you updated if I have future questions—we'll see if anything interesting comes up!