Using the maximum X-ray flux ratio and X-ray background to predict solar flare class

Abstract

Key Points:• To show the connection between X-ray flare class and background level • To show the usefulness of the X-ray background and ratio in flare prediction • To build a framework for an X-ray flare forecast using the GOES satellite Abstract We present the discovery of a relationship between the maximum ratio of the flare flux (namely, 0.5-4 Å to the 1-8 Å flux) and nonflare background (namely, the 1-8 Å background flux), which clearly separates flares into classes by peak flux level. We established this relationship based on an analysis of the Geostationary Operational Environmental Satellites X-ray observations of ∼ 50,000 X, M, C, and flares derived from the NOAA/Space Weather Prediction Center flares catalog. Employing a combination of machine learning techniques (K-nearest neighbors and nearest centroid algorithms) we show a separation of the observed parameters for the different peak flaring energies. This analysis is validated by successfully predicting the flare classes for 100% of the X-class flares, 76% of the M-class flares, 80% of the C-class flares, and 81% of the B-class flares for solar cycle 24, based on the training of the parametric extracts for solar flares in cycles 22-23.

Comparing SSN Index to X-Ray Flare and Coronal Mass Ejection Rates from Solar Cycles 22 – 24

“…We find that the normalization factor scales with SSN, which is highest when SSN is highest. Winter and Balasubramaniam (2015) found that the slope is flatter at lower solar activity levels. In the highest SSN bins, the fits were less reliable (high reduced χ 2 ).…”

“…However, for SSN > 200, the slopes appear consistent in Figure 3. As in Winter and Balasubramaniam (2015), we expect additional differences in the normalization factors and slopes for the flare rates between the solar cycles. For instance, we had shown that the normalization varied by up to ≈ 10 % and the slope varied by 9 % (solar maximum) to 16 % (rising phase) between SC 22, 23, and 24.…”

“…Aschwanden (2011) demonstrated this through their determination of the rates of the energetics, peak flux, and duration of X-ray flares in SC 21 -23 using observations from GOES. Similarly, we determined the occurrence rate for flare peak flux during the rising phase to solar maximum and solar maximum periods using the NOAA flare list 1 for SC 22 -24 (Winter and Balasubramaniam, 2015). Using the solar X-ray background analysis from Winter and Balasubramaniam (2014) .…”

Measuring X-ray anisotropy in solar flares. Prospective stereoscopic capabilities of STIX and MiSolFA

“…The statistical distribution of solar flare classes is well described by a power law behaviour, with spectral index of about 2.1 and percent-level variations in slope across different solar cycles. According to the recent review by Winter & Balasubramaniam (2015), one would expect to see at least 20 solar flares with class M1 or above per month during a solar maximum period. Together with our conservative estimate of the total overlapping live time of two months for STIX and MiSolFA, this implies that there should be at least 40 observations suitable for directivity measurement.…”

SeMiner: A Flexible Sequence Miner Method to Forecast Solar Time Series

“…However, the forecasting of future X-ray background level is important because that can also impact communication devices used on Earth. Besides that, background levels can also be used for solar flare forecasting [3]. There are two main approaches used to forecast solar flares: (1) forecasting with statistical methods based on data probability distribution; (2) forecasting with data mining techniques.…”