Kavli Affiliate: Nickolay Y. Gnedin
| First 5 Authors: David Robinson, Camille Avestruz, Nickolay Y. Gnedin, ,
| Summary:
Gas cooling and heating functions play a crucial role in galaxy formation.
But, it is computationally expensive to exactly compute these functions in the
presence of an incident radiation field. These computations can be greatly sped
up by using interpolation tables of pre-computed values, at the expense of
making significant and sometimes even unjustified approximations. Here we
explore the capacity of machine learning to approximate cooling and heating
functions with a generalized radiation field. Specifically, we use the machine
learning algorithm XGBoost to predict cooling and heating functions calculated
with the photoionization code Cloudy at fixed metallicity, using different
combinations of photoionization rates as features. We perform a constrained
quadratic fit in metallicity to enable a fair comparison with traditional
interpolation methods at arbitrary metallicity. We consider the relative
importance of various photoionization rates through both a principal component
analysis (PCA) and calculation of SHapley Additive exPlanation (SHAP) values
for our XGBoost models. We use feature importance information to select
different subsets of rates to use in model training. Our XGBoost models
outperform a traditional interpolation approach at each fixed metallicity,
regardless of feature selection. At arbitrary metallicity, we are able to
reduce the frequency of the largest cooling and heating function errors
compared to an interpolation table. We find that the primary bottleneck to
increasing accuracy lies in accurately capturing the metallicity dependence.
This study demonstrates the potential of machine learning methods such as
XGBoost to capture the non-linear behavior of cooling and heating functions.
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