Speaker
Description
Solar flares are the most energetic events in our solar system. Interacting with Earth’s magnetic field they may cause beautiful aurora or damage our modern infrastructure, but so far they cannot be reliably predicted. Novel methods such as neural networks allow us to cluster data, find rare events, or train models to attempt the prediction of solar flares. It is crucial to pre-process data such that the model can learn the relevant aspects of a phenomenon. A problem of flare data sets is that they always also contain non-flaring pixels, which may negatively affect any prediction models. We present a method using a variational autoencoder VAE to pre-process solar spectra and clean observations from Quiet Sun spectra which have no predictive power for solar flares.
