A method to deconvolve stellar rotational velocities II: The probability distribution function via Tikhonov regularization
Aims. Knowing the distribution of stellar rotational velocities is essential for understanding stellar evolution. Because we measure the projected rotational speed v sin i, we need to solve an ill-posed problem given by a Fredholm integral of the first kind to recover the "true" rotational...
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Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00046361_v595_n_p_Christen http://hdl.handle.net/20.500.12110/paper_00046361_v595_n_p_Christen |
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paper:paper_00046361_v595_n_p_Christen2023-06-08T14:28:17Z A method to deconvolve stellar rotational velocities II: The probability distribution function via Tikhonov regularization Rial, Diego Fernando Methods: data analysis Methods: numerical Methods: statistical Stars: fundamental parameters Stars: rotation Distribution functions Financial data processing Intelligent systems Monte Carlo methods Numerical methods Probability Probability distributions Problem solving Stars Statistical methods Velocity Velocity distribution Methods: numericals Methods:data analysis Methods:statistical Stars: Rotation Stars:fundamental parameters Probability density function Aims. Knowing the distribution of stellar rotational velocities is essential for understanding stellar evolution. Because we measure the projected rotational speed v sin i, we need to solve an ill-posed problem given by a Fredholm integral of the first kind to recover the "true" rotational velocity distribution. Methods. After discretization of the Fredholm integral we apply the Tikhonov regularization method to obtain directly the probability distribution function for stellar rotational velocities. We propose a simple and straightforward procedure to determine the Tikhonov parameter. We applied Monte Carlo simulations to prove that the Tikhonov method is a consistent estimator and asymptotically unbiased. Results. This method is applied to a sample of cluster stars. We obtain confidence intervals using a bootstrap method. Our results are in close agreement with those obtained using the Lucy method for recovering the probability density distribution of rotational velocities. Furthermore, Lucy estimation lies inside our confidence interval. Conclusions. Tikhonov regularization is a highly robust method that deconvolves the rotational velocity probability density function from a sample of v sin i data directly without the need for any convergence criteria. © 2016 ESO. Fil:Rial, D.F. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00046361_v595_n_p_Christen http://hdl.handle.net/20.500.12110/paper_00046361_v595_n_p_Christen |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Methods: data analysis Methods: numerical Methods: statistical Stars: fundamental parameters Stars: rotation Distribution functions Financial data processing Intelligent systems Monte Carlo methods Numerical methods Probability Probability distributions Problem solving Stars Statistical methods Velocity Velocity distribution Methods: numericals Methods:data analysis Methods:statistical Stars: Rotation Stars:fundamental parameters Probability density function |
spellingShingle |
Methods: data analysis Methods: numerical Methods: statistical Stars: fundamental parameters Stars: rotation Distribution functions Financial data processing Intelligent systems Monte Carlo methods Numerical methods Probability Probability distributions Problem solving Stars Statistical methods Velocity Velocity distribution Methods: numericals Methods:data analysis Methods:statistical Stars: Rotation Stars:fundamental parameters Probability density function Rial, Diego Fernando A method to deconvolve stellar rotational velocities II: The probability distribution function via Tikhonov regularization |
topic_facet |
Methods: data analysis Methods: numerical Methods: statistical Stars: fundamental parameters Stars: rotation Distribution functions Financial data processing Intelligent systems Monte Carlo methods Numerical methods Probability Probability distributions Problem solving Stars Statistical methods Velocity Velocity distribution Methods: numericals Methods:data analysis Methods:statistical Stars: Rotation Stars:fundamental parameters Probability density function |
description |
Aims. Knowing the distribution of stellar rotational velocities is essential for understanding stellar evolution. Because we measure the projected rotational speed v sin i, we need to solve an ill-posed problem given by a Fredholm integral of the first kind to recover the "true" rotational velocity distribution. Methods. After discretization of the Fredholm integral we apply the Tikhonov regularization method to obtain directly the probability distribution function for stellar rotational velocities. We propose a simple and straightforward procedure to determine the Tikhonov parameter. We applied Monte Carlo simulations to prove that the Tikhonov method is a consistent estimator and asymptotically unbiased. Results. This method is applied to a sample of cluster stars. We obtain confidence intervals using a bootstrap method. Our results are in close agreement with those obtained using the Lucy method for recovering the probability density distribution of rotational velocities. Furthermore, Lucy estimation lies inside our confidence interval. Conclusions. Tikhonov regularization is a highly robust method that deconvolves the rotational velocity probability density function from a sample of v sin i data directly without the need for any convergence criteria. © 2016 ESO. |
author |
Rial, Diego Fernando |
author_facet |
Rial, Diego Fernando |
author_sort |
Rial, Diego Fernando |
title |
A method to deconvolve stellar rotational velocities II: The probability distribution function via Tikhonov regularization |
title_short |
A method to deconvolve stellar rotational velocities II: The probability distribution function via Tikhonov regularization |
title_full |
A method to deconvolve stellar rotational velocities II: The probability distribution function via Tikhonov regularization |
title_fullStr |
A method to deconvolve stellar rotational velocities II: The probability distribution function via Tikhonov regularization |
title_full_unstemmed |
A method to deconvolve stellar rotational velocities II: The probability distribution function via Tikhonov regularization |
title_sort |
method to deconvolve stellar rotational velocities ii: the probability distribution function via tikhonov regularization |
publishDate |
2016 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00046361_v595_n_p_Christen http://hdl.handle.net/20.500.12110/paper_00046361_v595_n_p_Christen |
work_keys_str_mv |
AT rialdiegofernando amethodtodeconvolvestellarrotationalvelocitiesiitheprobabilitydistributionfunctionviatikhonovregularization AT rialdiegofernando methodtodeconvolvestellarrotationalvelocitiesiitheprobabilitydistributionfunctionviatikhonovregularization |
_version_ |
1768544529945722880 |