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.

Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of a reference library ranging from O type to M type stars.

This paper explores the application of Probabilistic Neural Network (PNN), SupportVector Machine (SVM) and k-means clustering as tools for automated classification of massive stellar spectra. The data set consists of a set of stellar spectra associated with the Sloan Digital Sky Survey (SDSS) SEGUE-1 and SEGUE-2, which consists of 400,000 data from 3850 to 8900Å with 3646 data points each. We investigate the application of principal components analysis (PCA) to reducing the dimensionality of data set to 280, 400 and 700 components.We show that PNN can give fairly accurate spectral type classifications σ RM S = 1.752, σ RM S = 1.538 and σ RM S = 1.391 and K-means can classify these spectra with an accuracy of σ RM S = 1.812, σ RM S = 1.731 and σ RM S = 1.654 and SVM with the accuracy of σ RM S = 1.795, σ RM S = 1.674 and σ RM S = 1.529 across the 280, 400 and 700 components, respectively. By using K-means the classification of the spectra renders 38 major classes. Furthermore, by comparing the results we noticed that PNN is more successful than K-means and arXiv:1609.03147v1 [astro-ph.IM] 11 Sep 2016 2 SVM in automated classification.

We present an automatic, fast, accurate and robust method of classifying astronomical objects. The Self Organizing Map (SOM) as an unsupervised Artificial Neural Network (ANN) algorithm is used for classification of stellar spectra of stars. The SOM is used to make clusters of different spectral classes of Jacoby, Hunter and Christian (JHC) library. This ANN technique needs no training examples and the stellar spectral data sets are directly fed to the network for the classification. The JHC library contains 161 spectra out of which, 158 spectra are selected for the classification. These 158 spectra are input vectors to the network and mapped into a two dimensional output grid. The input vectors close to each other are mapped into the same or neighboring neurons in the output space. So, the similar objects are making clusters in the output map and making it easy to analyze high dimensional data.After running the SOM algorithm on 158 stellar spectra, with 2799 data points each, the output map is analyzed and found that, there are 7 clusters in the output map corresponding to O to M stellar type. But, there are 12 misclassifications out of 158 and all of them are misclassified into the neighborhood of correct clusters which gives a success rate of about 92.4%.