Using the star formation histories ( SFHs ) near 94 supernova remnants ( SNRs ) , we infer the progenitor mass distribution for core-collapse supernovae .
We use Bayesian inference and model each SFH with multiple bursts of star formation ( SF ) , one of which is assumed to be associated with the SNR .
Assuming single-star evolution , the minimum mass of CCSNe is 7.33 ^ { +0.02 } _ { -0.16 } M _ { \odot } , the slope of the progenitor mass distribution is \alpha = -2.96 ^ { +0.45 } _ { -0.25 } , and the maximum mass is greater than M _ { \textrm { max } } > 59 M _ { \odot } with a 68 % confidence .
While these results are consistent with previous inferences , they also provide tighter constraints .
The progenitor distribution is somewhat steeper than a Salpeter initial mass function ( \alpha = -2.35 ) .
This suggests that either SNR catalogs are biased against the youngest SF regions , or the most massive stars do not explode as easily as lower mass stars .
If SNR catalogs are biased , it will most likely affect the slope but not the minimum mass .
The uncertainties are dominated by three primary sources of uncertainty , the SFH resolution , the number of SF bursts , and the uncertainty on SF rate in each age bin .
We address the first two of these uncertainties , with an emphasis on multiple bursts .
The third will be addressed in future work .