Mathematics – Probability
Scientific paper
Jan 1986
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1986rpspr...1..105a&link_type=abstract
In its USSR Report: Space (JPRS-USP-86-001) p 105-106 (SEE N86-20178 10-88) Transl. into ENGLISH from Izvestiya Vysshikh Uche
Mathematics
Probability
Energy Spectra, Solar Flares, Solar Radio Emission, Solar Wind, Space Plasmas, Sun, Amplitudes, Parameterization, Probability Density Functions
Scientific paper
Since one of the most important problems in studies of the physics of active regions on the Sun is an investigation of preflare processes in solar plasma, the authors examined some additional possibilities for obtaining information on processes in active regions by regarding them as plasma parametric systems. An analysis was made of fluctuations of preflare solar radio emission, including the nature of changes in amplitudes, energy spectra and probability distribution functions. Data were used on integral solar radio emission at a frequency of 755 MHz made at the Radioastrophysical Observatory, Latvian Academy of Sciences, using the RT-10 radio telescope from June through August 1979 to 1980, a period of the solar maximum. About 40 observations were selected, during which 12 powerful radiobursts associated with proton flares were observed. The pertinent parameters were analyzed before and after flares. A comparison of the results of observation of these fluctuations and the results of modeling indicated a modulation-multiplicative nature of the interaction between noise radiation and quasiperiodic processes on the Sun. It is concluded that the agreement of the dynamic effects of the probability density function obtained from the results of observations in the preflare period and the results of analog and mathematical modeling, as well as comparison of experimental studies of parametric systems, give a sound basis for regarding solar activity processes as processes in a parametric system. This finding will be useful in physical interpretation of preflare phenomena and in predicting solar activity.
Averyanikhina Y. A.
Kobrin M. M.
Orlov Ya. I.
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