The solar X-ray irradiance is significantly heightened during the course of a solar flare, which can cause radio blackouts due to ionization of the atoms in the ionosphere. As the duration of a solar flare is not related to the size of that flare, it is not directly clear how long those blackouts can persist. Using a random forest regression model trained on data taken from X-ray light curves, we have developed a direct forecasting method that predicts how long the event will remain above background levels. We test this on a large collection of flares observed with GOES-15, and show that it generally outperforms simple linear regression, giving a median error of less than 2 min for the approximate end time of a flare. This random forest model is computationally light enough to be performed in real time, allowing for the prediction to be made during the course of a flare.

中文翻译：

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预测正在进行的太阳耀斑的剩余持续时间

在太阳耀斑过程中，太阳 X 射线辐照度显着升高，由于电离层中原子的电离，这可能导致无线电中断。由于太阳耀斑的持续时间与耀斑的大小无关，因此尚不清楚这些停电会持续多久。使用基于 X 射线光曲线数据训练的随机森林回归模型，我们开发了一种直接预测方法，可以预测事件将保持在背景水平之上的时间。我们在使用 GOES-15 观察到的大量耀斑上对此进行了测试，并表明它通常优于简单的线性回归，对于耀斑的大致结束时间给出的中值误差小于 2 分钟。这个随机森林模型在计算上足够轻，可以实时执行，