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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | https://arxiv.org/abs/2603.08513 |
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| _version_ | 1866912955765882880 |
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| author | Bisht, Nikhil Collins, David C. |
| author_facet | Bisht, Nikhil Collins, David C. |
| contents | Giant Molecular Clouds (GMCs) are dominated by supersonic turbulence, creating a complex network of shocks and filaments that regulate star formation. While the global inefficiency of star formation is well-observed, predicting exactly which gas parcels within a turbulent cloud will collapse to form stars remains a challenge. In this work, we present a supervised machine learning framework to forecast the Lagrangian history of prestellar cores in magnetohydrodynamic (MHD) turbulence. We utilize Extreme Gradient Boosting (XGBoost) to train a regression model on the trajectories of $\sim 2.1$ million tracer particles evolved within a self-gravitating, turbulent MHD simulation.
By mapping the instantaneous phase-space state (position, velocity, and density) of gas parcels to their future coordinates, our model successfully predicts the 3D evolution of star-forming cores over a horizon of $\sim 0.45$ Myr ($0.25~t_{\rm ff}$). We achieve a global coefficient of determination of $R^2 > 0.99$ and demonstrate that the model captures the non-linear convergent flows characteristic of gravitational collapse. Crucially, we show that local phase-space information alone is sufficient to distinguish between transient density fluctuations and bound collapsing cores. This data-driven approach offers a computationally efficient alternative to traditional sink-particle algorithms and provides a pathway for developing high-fidelity subgrid models for galaxy-scale simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08513 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ML in Astrophysical Turbulence I: Predicting Prestellar Cores in Magnetized Molecular Clouds using eXtreme Gradient Boosting Bisht, Nikhil Collins, David C. Astrophysics of Galaxies Giant Molecular Clouds (GMCs) are dominated by supersonic turbulence, creating a complex network of shocks and filaments that regulate star formation. While the global inefficiency of star formation is well-observed, predicting exactly which gas parcels within a turbulent cloud will collapse to form stars remains a challenge. In this work, we present a supervised machine learning framework to forecast the Lagrangian history of prestellar cores in magnetohydrodynamic (MHD) turbulence. We utilize Extreme Gradient Boosting (XGBoost) to train a regression model on the trajectories of $\sim 2.1$ million tracer particles evolved within a self-gravitating, turbulent MHD simulation. By mapping the instantaneous phase-space state (position, velocity, and density) of gas parcels to their future coordinates, our model successfully predicts the 3D evolution of star-forming cores over a horizon of $\sim 0.45$ Myr ($0.25~t_{\rm ff}$). We achieve a global coefficient of determination of $R^2 > 0.99$ and demonstrate that the model captures the non-linear convergent flows characteristic of gravitational collapse. Crucially, we show that local phase-space information alone is sufficient to distinguish between transient density fluctuations and bound collapsing cores. This data-driven approach offers a computationally efficient alternative to traditional sink-particle algorithms and provides a pathway for developing high-fidelity subgrid models for galaxy-scale simulations. |
| title | ML in Astrophysical Turbulence I: Predicting Prestellar Cores in Magnetized Molecular Clouds using eXtreme Gradient Boosting |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2603.08513 |