The Panchromatic Hubble Andromeda Treasury IV. A Probabilistic Approach to Inferring the High Mass Stellar Initial Mass Function and Other Power-law Functions
Daniel R. Weisz, Morgan Fouesneau, David W. Hogg, Hans-Walter Rix,, Andrew E. Dolphin, Julianne J. Dalcanton, Daniel T. Foreman-Mackey, Dustin, Lang, L. Clifton Johnson, Lori C. Beerman, Eric F. Bell, Karl D. Gordon,, Dimitrios Gouliermis, Jason S. Kalirai, Evan D. Skillman

TL;DR
This paper introduces a probabilistic method for accurately inferring the high-mass stellar initial mass function in star clusters, addressing biases and uncertainties inherent in previous approaches.
Contribution
It presents a novel probabilistic framework that fully utilizes data, accounts for uncertainties, and avoids binning, enabling systematic study of the high-mass stellar IMF.
Findings
Estimates of the MF slope are unbiased with uncertainties depending on sample size and mass range.
Literature uncertainties are often underestimated compared to theoretical lower limits.
Correcting uncertainties reveals a mean MF slope of 2.46 with 0.35 dex dispersion.
Abstract
We present a probabilistic approach for inferring the parameters of the present day power-law stellar mass function (MF) of a resolved young star cluster. This technique (a) fully exploits the information content of a given dataset; (b) accounts for observational uncertainties in a straightforward way; (c) assigns meaningful uncertainties to the inferred parameters; (d) avoids the pitfalls associated with binning data; and (e) is applicable to virtually any resolved young cluster, laying the groundwork for a systematic study of the high mass stellar MF (M > 1 Msun). Using simulated clusters and Markov chain Monte Carlo sampling of the probability distribution functions, we show that estimates of the MF slope, {\alpha}, are unbiased and that the uncertainty, {\Delta}{\alpha}, depends primarily on the number of observed stars and stellar mass range they span, assuming that the…
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