Classification of probability densities on the basis of Pearson’s curves with application to coronal heating simulations

An important task for the problem of coronal heating is to produce reliable evaluation of the statistical properties of energy release and eruptive events such as micro-and nanoflares in the solar corona. Different types of distributions for the peak flux, peak count rate measurements, pixel int...

Full description

Bibliographic Details
Main Authors: O. Podladchikova, B. Lefebvre, V. Krasnoselskikh, V. Podladchikov
Format: Article
Language:English
Published: Copernicus Publications 2003-01-01
Series:Nonlinear Processes in Geophysics
Online Access:http://www.nonlin-processes-geophys.net/10/323/2003/npg-10-323-2003.pdf
id doaj-a9d6b17b48944b6cb240c52b0d00cf92
record_format Article
spelling doaj-a9d6b17b48944b6cb240c52b0d00cf922020-11-24T23:11:30ZengCopernicus PublicationsNonlinear Processes in Geophysics1023-58091607-79462003-01-01104/5323333Classification of probability densities on the basis of Pearson’s curves with application to coronal heating simulationsO. PodladchikovaB. LefebvreV. KrasnoselskikhV. PodladchikovAn important task for the problem of coronal heating is to produce reliable evaluation of the statistical properties of energy release and eruptive events such as micro-and nanoflares in the solar corona. Different types of distributions for the peak flux, peak count rate measurements, pixel intensities, total energy flux or emission measures increases or waiting times have appeared in the literature. This raises the question of a precise evaluation and classification of such distributions. For this purpose, we use the method proposed by K. Pearson at the beginning of the last century, based on the relationship between the first 4 moments of the distribution. Pearson's technique encompasses and classifies a broad range of distributions, including some of those which have appeared in the literature about coronal heating. This technique is successfully applied to simulated data from the model of Krasnoselskikh et al. (2002). It allows to provide successful fits to the empirical distributions of the dissipated energy, and to classify them as a function of model parameters such as dissipation mechanisms and threshold.http://www.nonlin-processes-geophys.net/10/323/2003/npg-10-323-2003.pdf
collection DOAJ
language English
format Article
sources DOAJ
author O. Podladchikova
B. Lefebvre
V. Krasnoselskikh
V. Podladchikov
spellingShingle O. Podladchikova
B. Lefebvre
V. Krasnoselskikh
V. Podladchikov
Classification of probability densities on the basis of Pearson’s curves with application to coronal heating simulations
Nonlinear Processes in Geophysics
author_facet O. Podladchikova
B. Lefebvre
V. Krasnoselskikh
V. Podladchikov
author_sort O. Podladchikova
title Classification of probability densities on the basis of Pearson’s curves with application to coronal heating simulations
title_short Classification of probability densities on the basis of Pearson’s curves with application to coronal heating simulations
title_full Classification of probability densities on the basis of Pearson’s curves with application to coronal heating simulations
title_fullStr Classification of probability densities on the basis of Pearson’s curves with application to coronal heating simulations
title_full_unstemmed Classification of probability densities on the basis of Pearson’s curves with application to coronal heating simulations
title_sort classification of probability densities on the basis of pearson’s curves with application to coronal heating simulations
publisher Copernicus Publications
series Nonlinear Processes in Geophysics
issn 1023-5809
1607-7946
publishDate 2003-01-01
description An important task for the problem of coronal heating is to produce reliable evaluation of the statistical properties of energy release and eruptive events such as micro-and nanoflares in the solar corona. Different types of distributions for the peak flux, peak count rate measurements, pixel intensities, total energy flux or emission measures increases or waiting times have appeared in the literature. This raises the question of a precise evaluation and classification of such distributions. For this purpose, we use the method proposed by K. Pearson at the beginning of the last century, based on the relationship between the first 4 moments of the distribution. Pearson's technique encompasses and classifies a broad range of distributions, including some of those which have appeared in the literature about coronal heating. This technique is successfully applied to simulated data from the model of Krasnoselskikh et al. (2002). It allows to provide successful fits to the empirical distributions of the dissipated energy, and to classify them as a function of model parameters such as dissipation mechanisms and threshold.
url http://www.nonlin-processes-geophys.net/10/323/2003/npg-10-323-2003.pdf
work_keys_str_mv AT opodladchikova classificationofprobabilitydensitiesonthebasisofpearsonscurveswithapplicationtocoronalheatingsimulations
AT blefebvre classificationofprobabilitydensitiesonthebasisofpearsonscurveswithapplicationtocoronalheatingsimulations
AT vkrasnoselskikh classificationofprobabilitydensitiesonthebasisofpearsonscurveswithapplicationtocoronalheatingsimulations
AT vpodladchikov classificationofprobabilitydensitiesonthebasisofpearsonscurveswithapplicationtocoronalheatingsimulations
_version_ 1725604102529875968