Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy. Being able to estimate these properties helps better understand CME dynamics. Our study is based on the CME catalog maintained at the Coordinated Data Analysis Workshops (CDAW) Data Center, which contains all CMEs manually identified since 1996 using the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO). We use LASCO C2 data in the period between January 1996 and December 2020 to train, validate and test DeepCME through 10-fold cross validation. The DeepCME method is a fusion of three deep learning models, including ResNet, InceptionNet, and InceptionResNet. Our fusion model extracts features from LASCO C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. Experimental results show that the fusion model yields a mean relative error (MRE) of 0.013 (0.009, respectively) compared to the MRE of 0.019 (0.017, respectively) of the best component model InceptionResNet (InceptionNet, respectively) in estimating the CME mass (kinetic energy, respectively). To our knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations.

翻译：日冕物质抛射（CME）是剧烈的太阳爆发事件，对地球具有重要影响。本文提出一种名为DeepCME的新方法，用于估计CME的两项属性：质量与动能。准确估计这些属性有助于深入理解CME的动力学机制。本研究基于协调数据分析研讨会（CDAW）数据中心维护的CME目录，该目录包含自1996年以来利用太阳和日光层探测器（SOHO）搭载的大角度光谱日冕仪（LASCO）人工识别的所有CME事件。我们采用1996年1月至2020年12月期间的LASCO C2数据，通过10折交叉验证对DeepCME进行训练、验证和测试。DeepCME方法融合了三种深度学习模型：ResNet、InceptionNet和InceptionResNet。该融合模型从LASCO C2图像中提取特征，有效结合三个子模型的学习能力，协同估计CME的质量与动能。实验结果表明，在估计CME质量（动能）时，融合模型的平均相对误差（MRE）为0.013（0.009），而最佳子模型InceptionResNet（InceptionNet）的MRE分别为0.019（0.017）。据我们所知，这是深度学习首次被应用于CME质量与动能的估计。