On the minimum number of radiation field parameters to specify gas cooling and heating functions
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 r...
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| Format: | Article |
| Language: | English |
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Maynooth Academic Publishing
2025-06-01
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| Series: | The Open Journal of Astrophysics |
| Online Access: | https://doi.org/10.33232/001c.141235 |
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| author | David Robinson Camille Avestruz Nickolay Y. Gnedin |
| author_facet | David Robinson Camille Avestruz Nickolay Y. Gnedin |
| author_sort | David Robinson |
| collection | DOAJ |
| description | 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 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)](https://ui.adsabs.harvard.edu/abs/2012ApJS..202...13G/abstract). 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. |
| format | Article |
| id | doaj-art-0de04c2dfe984d9c95af8908e923e69d |
| institution | DOAJ |
| issn | 2565-6120 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Maynooth Academic Publishing |
| record_format | Article |
| series | The Open Journal of Astrophysics |
| spelling | doaj-art-0de04c2dfe984d9c95af8908e923e69d2025-08-20T03:23:07ZengMaynooth Academic PublishingThe Open Journal of Astrophysics2565-61202025-06-01810.33232/001c.141235On the minimum number of radiation field parameters to specify gas cooling and heating functionsDavid RobinsonCamille AvestruzNickolay Y. GnedinFast 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 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)](https://ui.adsabs.harvard.edu/abs/2012ApJS..202...13G/abstract). 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.https://doi.org/10.33232/001c.141235 |
| spellingShingle | David Robinson Camille Avestruz Nickolay Y. Gnedin On the minimum number of radiation field parameters to specify gas cooling and heating functions The Open Journal of Astrophysics |
| title | On the minimum number of radiation field parameters to specify gas cooling and heating functions |
| title_full | On the minimum number of radiation field parameters to specify gas cooling and heating functions |
| title_fullStr | On the minimum number of radiation field parameters to specify gas cooling and heating functions |
| title_full_unstemmed | On the minimum number of radiation field parameters to specify gas cooling and heating functions |
| title_short | On the minimum number of radiation field parameters to specify gas cooling and heating functions |
| title_sort | on the minimum number of radiation field parameters to specify gas cooling and heating functions |
| url | https://doi.org/10.33232/001c.141235 |
| work_keys_str_mv | AT davidrobinson ontheminimumnumberofradiationfieldparameterstospecifygascoolingandheatingfunctions AT camilleavestruz ontheminimumnumberofradiationfieldparameterstospecifygascoolingandheatingfunctions AT nickolayygnedin ontheminimumnumberofradiationfieldparameterstospecifygascoolingandheatingfunctions |