A nanoflare model for active region radiance: application of artificial neural networks

Abstract

Context. Nanoflares are small impulsive bursts of energy that blend with and possibly make up much of the solar background emission. Determining their frequency and energy input is central to understanding the heating of the solar corona. One method is to extrapolate the energy frequency distribution of larger individually observed flares to lower energies. Only if the power law exponent is greater than 2 is it considered possible that nanoflares contribute significantly to the energy input. Aims. Time sequences of ultraviolet line radiances observed in the corona of an active region are modelled with the aim of determining the power law exponent of the nanoflare energy distribution. Methods. A simple nanoflare model based on three key parameters (the flare rate, the flare duration, and the power law exponent of the flare energy frequency distribution) is used to simulate emission line radiances from the ions Fe XIX, Ca XIII, and Si iii, observed by SUMER in the corona of an active region as it rotates around the east limb of the Sun. Light curve pattern recognition by an Artificial Neural Network (ANN) scheme is used to determine the values.Results. The power law exponents, α ≈ 2.8, 2.8, and 2.6 are obtained for Fe XIX, Ca XIII, and Si iii respectively.Conclusions. The light curve simulations imply a power law exponent greater than the critical value of 2 for all ion species. This implies that if the energy of flare-like events is extrapolated to low energies, nanoflares could provide a significant contribution to the heating of active region coronae.

New Orbital Parameters and Radial Velocity Curve Analysis of Spectroscopic Binary Stars

“…The PNN has four layers: input, pattern, summation, and output, respectively (see Fig. 5 in Bazarghan et al 2008). When an input vector is present, the pattern layer computes distances from the input vector to the training input vectors and produces a vector whose elements indicate how close the input is to a training input.…”

“…In recent years, ANNs have been widely used in astronomy for applications such as star/galaxy discrimination, morphological classification of galaxies, and spectral classification of stars (see Bazarghan et al 2008, and references therein). Following Bazarghan et al (2008), we employ Probabilistic Neural Networks (PNNs), which have been investigated in detail by Bazarghan et al (2008).…”

New Radial Velocity Studies and Curve Analysis of Spectroscopic Binary Systems CS22964-161, LV Her, RW Lac and HD 34700

“…The PNN has four layers including input, pattern, summation, and output layers, respectively (see Fig. 5 in Bazarghan et al [9]). When an input vector is presented, the pattern layer computes distances from the input vector to the training input vectors and produces a vector whose elements indicate how close the input is to a training input.…”

“…In recent years, ANNs have been widely used in astronomy for applications such as star/galaxy discrimination, morphological classification of galaxies, and spectral classification of stars (see Bazarghan et al [9] and references therein). Following Bazarghan et al [9], we employ Probabilistic Neural Networks (PNNs). This network has been investigated in ample details by Bazarghan et al [9].…”

“…Following Bazarghan et al [9], we employ Probabilistic Neural Networks (PNNs). This network has been investigated in ample details by Bazarghan et al [9]. Probabilistic Neural Network (PNN) is a new tool to derive the orbital parameters of the spectroscopic binary stars.…”