Populating Galaxies Into Halos Via Machine Learning on the Simba Simulation
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866918259776815104 |
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| author | Das, Pratyush Kumar Davé, Romeel Cui, Weiguang |
| author_facet | Das, Pratyush Kumar Davé, Romeel Cui, Weiguang |
| contents | We present a machine-learning framework, Machine Inferred Galaxy (MIG), to populate dark-matter haloes with galaxies in N-body simulations. MIG predicts stellar mass ($M_\ast$), star-formation rate (SFR), atomic and molecular gas masses ($M_{\mathrm{HI}}$ and $M_{\mathrm{H_2}}$), and metallicity, and can be extended to other properties and simulations. The pipeline first separates haloes into centrals and satellites, then uses classifiers to distinguish star-forming (SF) from quenched (Q) systems, followed by regressors trained on the SF subsets for both centrals and satellites. Trained on the $(100,h^{-1},\mathrm{Mpc})^3$ SIMBA galaxy-formation simulation at $z=0$, MIG achieves high accuracy for key baryonic properties (e.g. $R^2 \approx 0.9$ for $M_{\mathrm{HI}}$ of central galaxies), and remains robust at $z=1$ and $z=2$. Training on fractional quantities (e.g. $M_{\mathrm{HI}}/M_\ast$) and rescaling by predicted $M_\ast$ improves performance over direct predictions across properties and redshifts. MIG also reproduces galaxy mass distribution functions with higher fidelity, enabling accurate predictions of integrated tracers such as H I intensity maps. MIG therefore provides an efficient, physically consistent route to generate mock galaxy catalogues and baryonic tracers in large cosmological volumes for upcoming surveys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16103 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Populating Galaxies Into Halos Via Machine Learning on the Simba Simulation Das, Pratyush Kumar Davé, Romeel Cui, Weiguang Astrophysics of Galaxies We present a machine-learning framework, Machine Inferred Galaxy (MIG), to populate dark-matter haloes with galaxies in N-body simulations. MIG predicts stellar mass ($M_\ast$), star-formation rate (SFR), atomic and molecular gas masses ($M_{\mathrm{HI}}$ and $M_{\mathrm{H_2}}$), and metallicity, and can be extended to other properties and simulations. The pipeline first separates haloes into centrals and satellites, then uses classifiers to distinguish star-forming (SF) from quenched (Q) systems, followed by regressors trained on the SF subsets for both centrals and satellites. Trained on the $(100,h^{-1},\mathrm{Mpc})^3$ SIMBA galaxy-formation simulation at $z=0$, MIG achieves high accuracy for key baryonic properties (e.g. $R^2 \approx 0.9$ for $M_{\mathrm{HI}}$ of central galaxies), and remains robust at $z=1$ and $z=2$. Training on fractional quantities (e.g. $M_{\mathrm{HI}}/M_\ast$) and rescaling by predicted $M_\ast$ improves performance over direct predictions across properties and redshifts. MIG also reproduces galaxy mass distribution functions with higher fidelity, enabling accurate predictions of integrated tracers such as H I intensity maps. MIG therefore provides an efficient, physically consistent route to generate mock galaxy catalogues and baryonic tracers in large cosmological volumes for upcoming surveys. |
| title | Populating Galaxies Into Halos Via Machine Learning on the Simba Simulation |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2406.16103 |