ARCAFF: Active Region Classification and Flare Forecasting

太阳是一颗神秘的星星，它会产生太阳系中最强大的爆炸事件--太阳耀斑和日冕物质抛射。研究这些现象可以提供一个独特的机会，以更深入地了解太阳的基本过程，并更好地预测空间天气。活动区域分类和耀斑预测（ARCAFF）项目将开发一个超越最先进的耀斑预测系统，利用端到端深度学习（DL）模型来显着改善传统的耀斑预测能力。ARCAFF将提高当前业务耀斑预报产品的准确性和及时性，并创建新的时间序列耀斑预报。此外，ARCAFF预报将包括预报的不确定性，这是对现有系统的另一个重大改进。大量可用的天基太阳观测是这类分析的理想候选者，因为DL在模拟复杂关系方面很有效。DL已经成功地开发和部署在天气预报，金融服务和医疗保健领域，但尚未在太阳物理领域得到充分利用。来自ARCAFF的太阳耀斑预报将以使用国际社区标准的当前系统为基准，并将展示ARCAFF的上级预报能力。为ARCAFF开发的数据集，代码和DNNS将公开提供，以支持进一步的研究工作并鼓励其重复使用。ARCAFF与该工作计划相关，因为它将利用目前可用的数据空间天气数据来训练DL模型，以提高预报准确性。DL本身是一项创新技术，对DL模型的分析将提高对太阳耀斑的科学理解。通过创造新的预报产品，它将根据气象服务的最佳做法，为科学和监测目的开发和成熟新的概念。

The Sun is an enigmatic star that produces the most powerful explosive events in our solar system - solar flares and coronal mass ejections. Studying these phenomena can provide a unique opportunity to develop a deeper understanding of fundamental processes on the Sun, and critically, to better forecast space weather. The Active Region Classification and Flare Forecasting (ARCAFF) project will develop a beyond state-of-the-art flare forecasting system utilising end-to-end deep learning (DL) models to significantly improve upon traditional flare forecasting capabilities. ARCAFF will increase the accuracy and timeliness of current operational flare forecast products and create new time series flare forecasts. Furthermore, ARCAFF forecasts will include forecast uncertainties, another major improvement over current systems. The large amount of available space-based solar observations are an ideal candidate for this type of analysis, given DL effectiveness in modelling complex relationships. DL has already been successfully developed and deployed in weather forecasting, financial services, and health care domains, but has not been fully exploited in the solar physics domain. Solar flare forecasts from ARCAFF will be benchmarked against current systems using international community standards, and will demonstrate ARCAFF’s superior forecasting capabilities. The datasets, codes and DNNS developed for ARCAFF will be made openly available to support further research efforts and encourage their re-use. ARCAFF is relevant to the work program as it will exploit currently available data space weather data to train DL models to improve forecast accuracy. DL itself is an innovation enabling technology and analysis of the DL models will improve scientific understanding of solar flares. Through the creation of new forecast products it will develop and mature new concepts for both scientific and monitoring purposes, following the best-practices of meteorological services.