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Neutron Stars populations: bayesian statistics tools

Grant number: 16/09831-0
Support type:Regular Research Grants
Duration: February 01, 2017 - January 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Astronomy - Stellar Astrophysics
Principal Investigator:Rodolfo Valentim da Costa Lima
Grantee:Rodolfo Valentim da Costa Lima
Home Institution: Instituto de Ciências Ambientais, Químicas e Farmacêuticas (ICAQF). Universidade Federal de São Paulo (UNIFESP). Campus Diadema. Diadema , SP, Brazil


Neutron Stars (NSs) are the final stages of "life" of stars with different initial masses, knowledge of NSs populations is essential for understanding evolutionary ways of the progenitor stars, equations of state models and information about the matter in extreme density systems and temperature. For NSs, the mass is the main parameter observed that can be inferred by different methods: Shapiro Delay and Mass Function (Kepler's Third Law). Through Bayesian Inference and parameterization using gaussians can infer masses "canonical" to the NSs groups. Using the database available on the network (Figure 2), containing the mass and the measured deviations and different methods published by several authors, the analysis allows inferring these paths and estimate the characteristic masses and other relevant parameters (core density, for example). The project proposes to study the distribution of masses of ENs from public domain data, to infer the mass scale and the existence of a maximum value for ENs. (AU)

Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
JESUS, J. F.; VALENTIM, R.; ESCOBAL, A. A.; PEREIRA, S. H. Gaussian process estimation of transition redshift. Journal of Cosmology and Astroparticle Physics, n. 4 APR 2020. Web of Science Citations: 0.
VALENTIM, R.; JESUS, J. F. Entropy and creation cold dark matter models. ASTRONOMISCHE NACHRICHTEN, v. 340, n. 1-3, SI, p. 105-107, JAN-MAR 2019. Web of Science Citations: 0.
JESUS, J. F.; GREGORIO, T. M.; ANDRADE-OLIVEIRA, F.; VALENTIM, R.; MATOS, C. A. O. Bayesian correction of H(z) data uncertainties. Monthly Notices of the Royal Astronomical Society, v. 477, n. 3, p. 2867-2873, JUL 2018. Web of Science Citations: 5.
JESUS, J. F.; VALENTIM, R.; ANDRADE-OLIVERA, F. Bayesian analysis of CCDM models. Journal of Cosmology and Astroparticle Physics, n. 9 SEP 2017. Web of Science Citations: 1.

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