Abstract

The paper presents machine-learning models for predicting powerful solar flares and background X-ray fluxes in the range of 1–8 Å. To predict solar flares for the next day, information was used on the current level of solar activity obtained from ground-based synoptic observations, such as characteristics of sunspots and radio fluxes at wavelengths of 10.7 and 5 cm, as well as the level of the background flux and the number of solar flares of the current day obtained from the GOES satellite. To predict the background fluxes of X-ray radiation, only data from ground-based telescopes were used. The high efficiency of the forecast for the next day is shown. The neural network was trained on data available since 2002.

中文翻译：

---

基于天气的地面观测，使用机器学习模型预测太阳耀斑和X射线辐射的背景通量

摘要

本文介绍了机器学习模型，用于预测1–8Å范围内强大的太阳耀斑和背景X射线通量。为了预测第二天的太阳耀斑，使用了有关从地面天气观测获得的当前太阳活动水平的信息，例如太阳黑子的特征和波长为10.7和5 cm的无线电通量，以及太阳活动水平。从GOES卫星获得的背景通量和当日的太阳耀斑数量。为了预测X射线辐射的背景通量，仅使用了来自地面望远镜的数据。显示了第二天的高效率预测。自2002年以来，神经网络接受了可用数据的培训。