5:15 PM - 6:45 PM
[MGI29-P06] Exploring Potential Predictors of Solar Energetic Particles and Solar Flares Using Statistical Causal Discovery
Keywords:Statistical Causal Discovery, Solar Flare, Solar Energetic Particle
Fujitsu Ltd. and Nagoya University are jointly researching in the field of space weather, aiming to ensure the safety of human activities that are becoming more active in the geospace, the Moon, Mars, and interplanetary space.
Solar flares (SFs) and coronal mass ejections (CMEs), which are sudden phenomena on the sun, have an impact on people's lives and space systems. In particular, in deep space, Solar Energetic Particle (SEP) events, which accompany these phenomena, can directly affect human life, making them an important research subject in the field of space weather.
To improve the lead time for predicting the occurrence of SEP events and the regression prediction of particle quantities, it is necessary to explore the characteristics of solar active regions (ARs) that generate parent events such as flares and CMEs. Since the observational data for the ARs that produces SEP events are limited, it is also important to expand the information through numerical simulations.
We used statistical causal discovery technology developed by Fujitsu research, to investigate the magnetic field features of solar ARs for three objective variables: 1. X-ray peak of flares, 2. integrated intensity of flares, and 3. duration of flares. We used data obtained from the physics-based scheme to predict solar flares based on the three-dimensional magnetic field model around sunspots (Kusano et al. 2020, Science), as well as observational data from SDO and others. In this poster, we will present the results and discuss the predictive capability of the SEP-active flares.
Solar flares (SFs) and coronal mass ejections (CMEs), which are sudden phenomena on the sun, have an impact on people's lives and space systems. In particular, in deep space, Solar Energetic Particle (SEP) events, which accompany these phenomena, can directly affect human life, making them an important research subject in the field of space weather.
To improve the lead time for predicting the occurrence of SEP events and the regression prediction of particle quantities, it is necessary to explore the characteristics of solar active regions (ARs) that generate parent events such as flares and CMEs. Since the observational data for the ARs that produces SEP events are limited, it is also important to expand the information through numerical simulations.
We used statistical causal discovery technology developed by Fujitsu research, to investigate the magnetic field features of solar ARs for three objective variables: 1. X-ray peak of flares, 2. integrated intensity of flares, and 3. duration of flares. We used data obtained from the physics-based scheme to predict solar flares based on the three-dimensional magnetic field model around sunspots (Kusano et al. 2020, Science), as well as observational data from SDO and others. In this poster, we will present the results and discuss the predictive capability of the SEP-active flares.