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DeFN-R

DeFN

Solar Flare AI (DeFN-R)

Info: Deep Flare Net (DeFN-R) ・1600A filter. ・Deep Flare Net-Reliable (DeFN-R) ・Our prediction model using deep neural networks, named Deep Flare Net (DeFN), obtains solar observation data in real time and predicts solar flares in the next 24 hr. ・Deep Flare Net-Reliable (DeFN-R) is an extension of the original forecast model DeFN for probabilistic forecasting, with improved reliability over DeFN. ・The scale of the flare is called X, M, and C class from the largest to the smallest. DeFN-R forecasts the probability of X-class, M-class or higher, and C-class or higher flares. ・The bar graph shows the forecasted probability of a flare. We achieved a hight level of confidence with a small difference between the forecast probability and the frequency of occurrence by DeFN-R. ・If you want to predict whether a flare will occur or not, you need to set a probability threshold. When the probability threshold is set to the median of the flare occurrence distribution, it reproduces the same performance as DeFN. ・The probability of occurrence P for the full solar disk of M-class or higher is displayed in the upper right corner. When the probability of occurrence in each region is p₁, p₂, p₃…, it is calculated by P=1-(1-p₁)(1-p₂)(1-p₃)…. ・See DeFN-R performance more in detail in the following paper. – Nishizuka et al. 2020, The Astrophysical J., 899, 150 ・The database and code of DeFN model are released free. – Released DeFN Database (WDC@NICT) – Released Code of DeFN (GitHub) Acknowledgement: The data used here are courtesy of SDO/NASA, GOES/NOAA and SDO-JSOC team (Stanford University, LMSAL and NASA), DeepFlareNet

Solar Flare AI (DeFN-R)

Info: Deep Flare Net (DeFN-R)

・1600A filter.

・Deep Flare Net-Reliable (DeFN-R)

・Our prediction model using deep neural networks, named Deep Flare Net (DeFN), obtains solar observation data in real time and predicts solar flares in the next 24 hr.

・Deep Flare Net-Reliable (DeFN-R) is an extension of the original forecast model DeFN for probabilistic forecasting, with improved reliability over DeFN.

・The scale of the flare is called X, M, and C class from the largest to the smallest. DeFN-R forecasts the probability of X-class, M-class or higher, and C-class or higher flares.

・The bar graph shows the forecasted probability of a flare. We achieved a hight level of confidence with a small difference between the forecast probability and the
frequency of occurrence by DeFN-R.

・If you want to predict whether a flare will occur or not, you need to set a probability threshold. When the probability threshold is set to the median of the flare occurrence distribution, it reproduces the same performance as DeFN.

・The probability of occurrence P for the full solar disk of M-class or higher is displayed in the upper right corner. When the probability of occurrence in each region is
p₁, p₂, p₃…, it is calculated by P=1-(1-p₁)(1-p₂)(1-p₃)….

・See DeFN-R performance more in detail in the following paper.
– Nishizuka et al. 2020, The Astrophysical J., 899, 150

・The database and code of DeFN model are released free.
– Released DeFN Database (WDC@NICT)
– Released Code of DeFN (GitHub)

Acknowledgement: The data used here are courtesy of SDO/NASA, GOES/NOAA and SDO-JSOC team (Stanford University, LMSAL and NASA), DeepFlareNet

Solar Flare AI (DeFN)

Info: Deep Flare Net (DeFN)

・1600A filter.

・Our prediction model using deep neural networks, named Deep Flare Net (DeFN),
obtains solar observation data in real time and predicts solar flares in the next 24 hr.

・Solar flares are classified into X, M, and C-class flares. DeFN model is designed for
deterministic forecast of X-class, >=M-class and >=C-class flares.

・The training data consists of the line-of-sight and vector magnetograms (HMI/SDO),
131A and 1600A images (AIA/SDO) and the soft X-ray data (GOES).

・Solar flares occur in active regions, where magnetic field is strong around sunspots.
DeFN automatically detects active regions with strong magnetic field (>40 Gauss).

・Few flares occur in quiet regions where magnetic field is weak, but they are not
predicted by DeFN, as well as limb flares. DeFN skipps when the lack of full data.

・Detected regions are numbered for each prediction. The area No. corresponds to No.
in the right graph. Different three models are used for X,>M,>C-class predictions.

・When the bar graph exceeds 50%, we predict that a flare will occur.
50% corresponds to the median of the distribution of flare frequency. DeFN is unique
that there are few missed flares.

・The probability of occurrence P for the full solar disk of M-class or higher is displayed
in the upper right corner. When the probability of occurrence in each region is
p₁, p₂, p₃…, it is calculated by P=1-(1-p₁)(1-p₂)(1-p₃)….

・See DeFN performances more in detail in the following papers.
– Nishizuka et al. 2021, Earth, Planets and Space, 73, 64
– Nishizuka et al. 2018, The Astrophysical J., 858, 113
– Nishizuka et al. 2017, The Astrophysical J., 835, 156

・The database and code of DeFN model are released free.
– Released DeFN Database (WDC@NICT)
– Released Code of DeFN (GitHub)

Acknowledgement: The data used here are courtesy of SDO/NASA, GOES/NOAA and
SDO-JSOC team (Stanford University, LMSAL and NASA), DeepFlareNet