Observing and forecasting coronal mass ejections (CME) in real-time is crucial due to the strong geomagnetic storms they can generate that can have a potentially damaging effect, for example, on satellites and electrical devices. With its near-real-time availability, STEREO/HI beacon data is the perfect candidate for early forecasting of CMEs. However, previous work concluded that CME arrival prediction based on beacon data could not achieve the same accuracy as with high-resolution science data due to data gaps and lower quality. We present our novel machine-learning pipeline entitled ``Beacon2Science'', bridging the gap between beacon and science data to improve CME tracking. Through this pipeline, we first enhance the quality (signal-to-noise ratio and spatial resolution) of beacon data. We then increase the time resolution of enhanced beacon images through learned interpolation to match science data's 40-minute resolution. We maximize information coherence between consecutive frames with adapted model architecture and loss functions through the different steps. The improved beacon images are comparable to science data, showing better CME visibility than the original beacon data. Furthermore, we compare CMEs tracked in beacon, enhanced beacon, and science images. The tracks extracted from enhanced beacon data are closer to those from science images, with a mean average error of $\sim 0.5 ^\circ$ of elongation compared to $1^\circ$ with original beacon data. The work presented in this paper paves the way for its application to forthcoming missions such as Vigil and PUNCH.

翻译：实时观测和预报日冕物质抛射（CME）至关重要，因为它们可能引发强烈的磁暴，对卫星和电子设备等造成潜在损害。STEREO/HI信标数据具备近实时可用性，是早期预报CME的理想选择。然而，先前研究指出，由于数据缺失和质量较低，基于信标数据的CME到达预测无法达到与高分辨率科学数据相同的精度。本文提出名为“Beacon2Science”的新型机器学习流程，旨在弥合信标数据与科学数据之间的差距以改进CME追踪。该流程首先提升信标数据的质量（信噪比与空间分辨率），随后通过学习型插值将增强后信标图像的时间分辨率提高至与科学数据相同的40分钟分辨率。我们通过在不同步骤中调整模型架构和损失函数，最大化连续帧间的信息一致性。改进后的信标图像与科学数据相当，在CME可见性方面优于原始信标数据。此外，我们比较了在信标图像、增强信标图像和科学图像中追踪到的CME轨迹。从增强信标数据提取的轨迹更接近科学图像结果，其平均伸长角误差约为0.5°，而原始信标数据的误差为1°。本文工作为未来任务（如Vigil和PUNCH任务）的应用奠定了基础。