A method to deconvolve stellar rotational velocities
Aims. Rotational speed is an important physical parameter of stars, and knowing the distribution of stellar rotational velocities is essential for understanding stellar evolution. However, rotational speed cannot be measured directly and is instead the convolution between the rotational speed and th...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_00046361_v565_n_p_Cure |
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todo:paper_00046361_v565_n_p_Cure2023-10-03T14:00:49Z A method to deconvolve stellar rotational velocities Curé, M. Rial, D.F. Christen, A. Cassetti, J. Methods:analytical Methods:data analysis Methods:numerical Methods:statistical Stars:fundamental parameters Stars:rotation Inverse problems Numerical methods Stars Velocity Velocity distribution Methods: numericals Methods:analytical Methods:data analysis Methods:statistical Stars:fundamental parameters Stars:rotation Maximum entropy methods Aims. Rotational speed is an important physical parameter of stars, and knowing the distribution of stellar rotational velocities is essential for understanding stellar evolution. However, rotational speed cannot be measured directly and is instead the convolution between the rotational speed and the sine of the inclination angle v sin i. Methods. We developed a method to deconvolve this inverse problem and obtain the cumulative distribution function for stellar rotational velocities extending the work of Chandrasekhar & Münch (1950, ApJ, 111, 142) Results. This method is applied: a) to theoretical synthetic data recovering the original velocity distribution with a very small error; and b) to a sample of about 12.000 field main-sequence stars, corroborating that the velocity distribution function is non-Maxwellian, but is better described by distributions based on the concept of maximum entropy, such as Tsallis or Kaniadakis distribution functions. Conclusions. This is a very robust and novel method that deconvolves the rotational velocity cumulative distribution function from a sample of v sin i data in a single step without needing any convergence criteria. © ESO, 2014. Fil:Rial, D.F. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00046361_v565_n_p_Cure |
| 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:analytical Methods:data analysis Methods:numerical Methods:statistical Stars:fundamental parameters Stars:rotation Inverse problems Numerical methods Stars Velocity Velocity distribution Methods: numericals Methods:analytical Methods:data analysis Methods:statistical Stars:fundamental parameters Stars:rotation Maximum entropy methods |
| spellingShingle |
Methods:analytical Methods:data analysis Methods:numerical Methods:statistical Stars:fundamental parameters Stars:rotation Inverse problems Numerical methods Stars Velocity Velocity distribution Methods: numericals Methods:analytical Methods:data analysis Methods:statistical Stars:fundamental parameters Stars:rotation Maximum entropy methods Curé, M. Rial, D.F. Christen, A. Cassetti, J. A method to deconvolve stellar rotational velocities |
| topic_facet |
Methods:analytical Methods:data analysis Methods:numerical Methods:statistical Stars:fundamental parameters Stars:rotation Inverse problems Numerical methods Stars Velocity Velocity distribution Methods: numericals Methods:analytical Methods:data analysis Methods:statistical Stars:fundamental parameters Stars:rotation Maximum entropy methods |
| description |
Aims. Rotational speed is an important physical parameter of stars, and knowing the distribution of stellar rotational velocities is essential for understanding stellar evolution. However, rotational speed cannot be measured directly and is instead the convolution between the rotational speed and the sine of the inclination angle v sin i. Methods. We developed a method to deconvolve this inverse problem and obtain the cumulative distribution function for stellar rotational velocities extending the work of Chandrasekhar & Münch (1950, ApJ, 111, 142) Results. This method is applied: a) to theoretical synthetic data recovering the original velocity distribution with a very small error; and b) to a sample of about 12.000 field main-sequence stars, corroborating that the velocity distribution function is non-Maxwellian, but is better described by distributions based on the concept of maximum entropy, such as Tsallis or Kaniadakis distribution functions. Conclusions. This is a very robust and novel method that deconvolves the rotational velocity cumulative distribution function from a sample of v sin i data in a single step without needing any convergence criteria. © ESO, 2014. |
| format |
JOUR |
| author |
Curé, M. Rial, D.F. Christen, A. Cassetti, J. |
| author_facet |
Curé, M. Rial, D.F. Christen, A. Cassetti, J. |
| author_sort |
Curé, M. |
| title |
A method to deconvolve stellar rotational velocities |
| title_short |
A method to deconvolve stellar rotational velocities |
| title_full |
A method to deconvolve stellar rotational velocities |
| title_fullStr |
A method to deconvolve stellar rotational velocities |
| title_full_unstemmed |
A method to deconvolve stellar rotational velocities |
| title_sort |
method to deconvolve stellar rotational velocities |
| url |
http://hdl.handle.net/20.500.12110/paper_00046361_v565_n_p_Cure |
| work_keys_str_mv |
AT curem amethodtodeconvolvestellarrotationalvelocities AT rialdf amethodtodeconvolvestellarrotationalvelocities AT christena amethodtodeconvolvestellarrotationalvelocities AT cassettij amethodtodeconvolvestellarrotationalvelocities AT curem methodtodeconvolvestellarrotationalvelocities AT rialdf methodtodeconvolvestellarrotationalvelocities AT christena methodtodeconvolvestellarrotationalvelocities AT cassettij methodtodeconvolvestellarrotationalvelocities |
| _version_ |
1807316259410280448 |