ARCAFF: Active Region Classification and Flare Forecasting

ARCAFF：活动区域分类和耀斑预测

• 批准号: 10059394
• 金额: $ 19.63万
• 依托单位: UNIVERSITY OF WESTMINSTER
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10059394
• 关键词: ARCAFF; Active; Region; Classification; Flare

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.

太阳是一颗神秘的恒星，它会产生太阳系中最强大的爆炸性事件--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，加深对太阳基本过程的理解，更重要的是，可以更好地预报空间天气。活跃区域分类和耀斑预测(ARCAFF)项目将开发一种超越最先进水平的耀斑预报系统，利用端到端深度学习(DL)模型来显著改进传统的耀斑预报能力。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的预测将包括预测的不确定性，这是对当前系统的另一项重大改进。鉴于天基太阳观测在模拟复杂关系方面的有效性，大量可用的天基太阳观测是这类分析的理想候选者。目前已在天气预报、金融服务和医疗保健等领域成功开发和部署了DL，但尚未在太阳物理领域得到充分开发。来自ARCAFF的太阳耀斑预报将与目前使用国际社会标准的系统进行基准比较，并将展示ARCAFF的卓越预报能力。将公开提供为ARCAFF开发的数据集、代码和DNN，以支持进一步的研究努力并鼓励其重复使用。ARCAFF与工作计划相关，因为它将利用目前可用的数据空间天气数据来训练DL模式，以提高预报精度。DL本身是一项创新，使能技术和对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，它将遵循气象服务的最佳做法，为科学和监测目的发展和成熟新概念。