  Connecting observations of core-collapse supernova explosions to the
properties of their massive star progenitors is a long-sought, and challenging,
goal of supernova science. Recently, Barker et al. (2022) presented bolometric
light curves for a landscape of progenitors from spherically symmetric
neutrino-driven core-collapse supernova (CCSN) simulations using an effective
model. They find a tight relationship between the plateau luminosity of the
Type II-P CCSN light curve and the terminal iron core mass of the progenitor.
Remarkably, this allows us to constrain progenitor properties with photometry
alone. We analyze a large observational sample of Type II-P CCSN light curves
and estimate a distribution of iron core masses using the relationship of
Barker et al 2022. The inferred distribution matches extremely well with the
distribution of iron core masses from stellar evolutionary models, and namely,
contains high-mass iron cores that suggest contributions from very massive
progenitors in the observational data. We use this distribution of iron core
masses to infer minimum and maximum mass of progenitors in the observational
data. Using Bayesian inference methods to locate optimal initial mass function
parameters, we find M$_{\mathrm{min}}=9.8^{+0.37}_{-0.27}$ and
M$_{\mathrm{max}}=24.0^{+3.9}_{-1.9}$ solar masses for the observational data.
