Kavli Affiliate: Nickolay Y. Gnedin
| First 5 Authors: David Robinson, Camille Avestruz, Nickolay Y. Gnedin, ,
| Summary:
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 computed by Cloudy in the presence of
a generalized incident local radiation field. 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 SHapley Additive
exPlanation (SHAP) value 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,
with errors $gtrsim 10$ times smaller than for the interpolation table of
Gnedin and Hollon (2012). 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.
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