Issue |
A&A
Volume 565, May 2014
|
|
---|---|---|
Article Number | A85 | |
Number of page(s) | 7 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/201323344 | |
Published online | 15 May 2014 |
A method to deconvolve stellar rotational velocities
1 Instituto de Física y Astronomía, Universidad de Valparaíso, 2340000 Valparaíso, Chile
e-mail: michel.cure@uv.cl
2 Departamento de Matemáticas, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, 428 Buenos Aires, Argentina
e-mail: drial@mate.uba.ar
3 Instituto de Estadística, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
e-mail: alejandra.christen@ucv.cl
4 Universidad Nacional de General Sarmiento, 1613 Los Polvorines, Buenos Aires, Argentina
e-mail: jcassett@ungs.edu.ar
Received: 27 December 2013
Accepted: 13 March 2014
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.
Key words: methods: data analysis / methods: numerical / methods: statistical / stars: rotation / stars: fundamental parameters / methods: analytical
© ESO, 2014
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