On the minimum number of radiation field parameters to specify gas cooling and heating functions
David Robinson, Camille Avestruz, Nickolay Y. Gnedin

TL;DR
This paper demonstrates that only three energy bins are needed to accurately predict gas cooling and heating functions in galaxy simulations using machine learning, simplifying the modeling of radiation fields.
Contribution
The study identifies a minimal set of three energy bins that effectively predict gas cooling and heating functions, improving computational efficiency in astrophysical simulations.
Findings
Three energy bins provide comparable accuracy to six bins in predictions.
Machine learning models outperform previous interpolation methods by over an order of magnitude.
A subset of three photoionization rates can substitute for energy bins in modeling.
Abstract
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 (, and ) with the largest importance and train additional…
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