Active-Region Monitoring and Flare Forecasting – I. Data Processing and First Results

By Peter T. Gallagher, Y. Moon, Haimin Wang. 2002

180 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/bec3b83bc2f5c0e6d71febb68482c636a25df7b4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. G. Barnes
2. C. Schrijver
3. Na Deng
4. Chang Liu
5. Haimin Wang
6. Jason Tsong-Li Wang
7. K. Leka
8. D. S. Bloomfield

In Astronomy and Astrophysics

1. C. Schrijver
2. G. Barnes
3. K. Leka
4. Haimin Wang
5. Chang Liu
6. Na Deng
7. Jason Tsong-Li Wang
8. Manolis K. Georgoulis

Citations in the corpus

• TOWARD RELIABLE BENCHMARKING OF SOLAR FLARE FORECASTING METHODS (2012, methodology): “A sample of the techniques employed includes Poisson statistics (Gallagher et al. 2002), Bayesian statistics (Wheatland 2005), support vector machines (Li et al. 2007), discriminant analysis (Barnes et al. 2007), ordinal logistic regression (Song et al. 2009; Yuan et al. 2010), neural networks…”
• Photospheric Magnetic Field Properties of Flaring versus Flare-quiet Active Regions. IV. A Statistically Significant Sample (2007, background): “…designation (McIntosh 1990; Bornmann&Shaw1994), which is still the primary basis of most flare forecastingmethods evenwhen other information is considered, such as coronal morphology and line-of-sight magnetic morphology (NOAA/Space Environment Center [NOAA/SEC], and see Gallagher et al. 2002).”
• Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Products and the Random Forest Algorithm (2017, methodology): “For examples, Gallagher et al. (2002) and Bloomfield et al. (2012) used the McIntosh sunspot classification system and Poisson statistics to estimate probabilities of an AR to produce flares with different magnitude in 24 hours.”
• A COMPARISON OF FLARE FORECASTING METHODS. I. RESULTS FROM THE “ALL-CLEAR” WORKSHOP (2016, methodology): “The McIntosh–Poisson method uses the historical flare rates from McIntosh active region classifications to make forecasts using Poisson probabilities (Gallagher et al. 2002; Bloomfield et al. 2012).”