ML in Astrophysical Turbulence I: Predicting Prestellar Cores in Magnetized Molecular Clouds using eXtreme Gradient Boosting

Name: ML in Astrophysical Turbulence I: Predicting Prestellar Cores in Magnetized Molecular Clouds using eXtreme Gradient Boosting
Uploaded: 2026-10-23T03:53:28.188330+00:00
Duration: 337 s
Description: Researchers used an XGBoost machine learning model to predict where prestellar gas parcels will evolve over 0.45 million years in turbulent, magnetized molecular clouds. The model successfully predicted 3D trajectories with high accuracy, showing that local position, velocity, and density are key factors in identifying gravitationally bound cores.

Researchers used an XGBoost machine learning model to predict where prestellar gas parcels will evolve over 0.45 million years in turbulent, magnetized molecular clouds. The model successfully predicted 3D trajectories with high accuracy, showing that local position, velocity, and density are key factors in identifying gravitationally bound cores.

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