The Panchromatic Hubble Andromeda Treasury IV. A Probabilistic Approach   to Inferring the High Mass Stellar Initial Mass Function and Other Power-law   Functions
  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 uncertainties on individual masses and the completeness are well-characterized. Using idealized mock data, we compute the lower limit precision on {\alpha} and provide an analytic approximation for {\Delta}{\alpha} as a function of the observed number of stars and mass range. We find that ~ 3/4 of quoted literature uncertainties are smaller than the theoretical lower limit. By correcting these uncertainties to the theoretical lower limits, we find the literature studies yield <{\alpha}>=2.46 with a 1-{\sigma} dispersion of 0.35 dex. We verify that it is impossible for a power-law MF to obtain meaningful constraints on the upper mass limit of the IMF. We show that avoiding substantial biases in the MF slope requires: (1) including the MF as a prior when deriving individual stellar mass estimates; (2) modeling the uncertainties in the individual stellar masses; and (3) fully characterizing and then explicitly modeling the completeness for stars of a given mass. (abridged) 
Daniel R. Weisz, Morgan Fouesneau, David W. Hogg, Hans-Walter Rix,   Andrew E. Dolphin, Julianne J. Dalcanton, Daniel T. For...
astro-ph.CO astro-ph.GA