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.

使用监督机器学习建立了一个标准化分类太阳耀斑的过程，并发现了 C-Support Vector Machine (SVC) 算法在分类太阳耀斑中的出色表现。该模型具有较高的分类准确度，在检测和分类太阳耀斑方面表现出了很强的适应性和准确性。未来的工作可以集中于提高分类准确度、研究其他模型的实用性以及通过增加更多的数据集来扩展该框架的应用。