Open Access
2019 A preferential attachment model for the stellar initial mass function
Jessi Cisewski-Kehe, Grant Weller, Chad Schafer
Electron. J. Statist. 13(1): 1580-1607 (2019). DOI: 10.1214/19-EJS1556


Accurate specification of a likelihood function is becoming increasingly difficult in many inference problems in astronomy. As sample sizes resulting from astronomical surveys continue to grow, deficiencies in the likelihood function lead to larger biases in key parameter estimates. These deficiencies result from the oversimplification of the physical processes that generated the data, and from the failure to account for observational limitations. Unfortunately, realistic models often do not yield an analytical form for the likelihood. The estimation of a stellar initial mass function (IMF) is an important example. The stellar IMF is the mass distribution of stars initially formed in a given cluster of stars, a population which is not directly observable due to stellar evolution and other disruptions and observational limitations of the cluster. There are several difficulties with specifying a likelihood in this setting since the physical processes and observational challenges result in measurable masses that cannot legitimately be considered independent draws from an IMF. This work improves inference of the IMF by using an approximate Bayesian computation approach that both accounts for observational and astrophysical effects and incorporates a physically-motivated model for star cluster formation. The methodology is illustrated via a simulation study, demonstrating that the proposed approach can recover the true posterior in realistic situations, and applied to observations from astrophysical simulation data.


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Jessi Cisewski-Kehe. Grant Weller. Chad Schafer. "A preferential attachment model for the stellar initial mass function." Electron. J. Statist. 13 (1) 1580 - 1607, 2019.


Received: 1 July 2018; Published: 2019
First available in Project Euclid: 16 April 2019

zbMATH: 07056158
MathSciNet: MR3939305
Digital Object Identifier: 10.1214/19-EJS1556

Keywords: Approximate Bayesian Computation , Astrostatistics , computational statistics , dependent data

Vol.13 • No. 1 • 2019
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