  Fast and accurate approximations of gas cooling and heating functions are
needed for hydrodynamic galaxy simulations. We use machine learning to analyze
atomic gas cooling and heating functions in the presence of a generalized
incident local radiation field computed by Cloudy. We characterize the
radiation field through binned radiation field intensities instead of the
photoionization rates used in our previous work. We find a set of 6 energy bins
whose intensities exhibit relatively low correlation. We use these bins as
features to train machine learning models to predict Cloudy cooling and heating
functions at fixed metallicity. We compare the relative SHAP importance of the
features. From the SHAP analysis, we identify a feature subset of 3 energy bins
($0.5-1, 1-4$, and $13-16 \, \mathrm{Ry}$) with the largest importance and
train additional models on this subset. We compare the mean squared errors and
distribution of errors on both the entire training data table and a randomly
selected 20% test set withheld from model training. The machine learning models
trained with 3 and 6 bins, as well as 3 and 4 photoionization rates, have
comparable accuracy everywhere. We conclude that 3 energy bins (or 3 analogous
photoionization rates: molecular hydrogen photodissociation, neutral hydrogen
HI, and fully ionized carbon CVI) are sufficient to characterize the dependence
of the gas cooling and heating functions on our assumed incident radiation
field model.
