Detecting and Classifying Flares in High-Resolution Solar Spectra with Supervised Machine Learning
This work addresses the need to analyze flare contamination in exoplanet transmission spectra for astronomers, but it is incremental as it applies standard machine learning methods to a specific domain.
The paper tackled the problem of detecting and classifying solar flares in high-resolution spectra using supervised machine learning, achieving an average accuracy of 0.65 with specific classes scoring over 0.70, compared to a blind classification baseline of 0.33.
Flares are a well-studied aspect of the Sun's magnetic activity. Detecting and classifying solar flares can inform the analysis of contamination caused by stellar flares in exoplanet transmission spectra. In this paper, we present a standardized procedure to classify solar flares with the aid of supervised machine learning. Using flare data from the RHESSI mission and solar spectra from the HARPS-N instrument, we trained several supervised machine learning models, and found that the best performing algorithm is a C-Support Vector Machine (SVC) with non-linear kernels, specifically Radial Basis Functions (RBF). The best-trained model, SVC with RBF kernels, achieves an average aggregate accuracy score of 0.65, and categorical accuracy scores of over 0.70 for the no-flare and weak-flare classes, respectively. In comparison, a blind classification algorithm would have an accuracy score of 0.33. Testing showed that the model is able to detect and classify solar flares in entirely new data with different characteristics and distributions from those of the training set. Future efforts could focus on enhancing classification accuracy, investigating the efficacy of alternative models, particularly deep learning models, and incorporating more datasets to extend the application of this framework to stars that host exoplanets.