July 2026
A Probabilistic Machine Learning Approach to Emulate Chemistry in Astrophysical Simulations (Lennart Buhlmann & Felix Rauprich)
Author: Birka Zimmermann
Dense cores observed with ALMA are often treated as direct tracers of the material available to form stars, but their inferred masses depend strongly on projection, flux recovery, and the assumed dust temperature.
In this work, we connect radiation-hydrodynamic simulations of collapsing massive clumps with synthetic ALMAGAL-like observations to quantify these effects. We find that projected core masses can exceed the true spherical masses by factors of about 2–3, while interferometric flux reconstruction and temperature uncertainties add further scatter. A single clump-scale luminosity-to-mass-based temperature prescription, commonly used in observational studies, captures the global evolution but misses the core-to-core temperature variations. Including local flux information, and especially using a machine-learning-based temperature estimate, improves the recovery of individual core temperatures and masses.
Overall, our results show that ALMA continuum-derived core masses are typically uncertain by factors of a few, and that synthetic observations provide a practical way to calibrate these biases for surveys such as ALMAGAL.
A Probabilistic Machine Learning Approach to Emulate Chemistry in Astrophysical Simulations (Lennart Buhlmann & Felix Rauprich)
How reliably can core masses be inferred from ALMA continuum emission? (Birka Zimmermann)
Following Tracer Particles to Identify Filaments in Star-Forming Cores with HDBSCAN (Nuray Ortaköse)
Estimating ionization fractions in SILCC simulations (Lennart Buhlmann)
1D protostellar disk sub-grid model for star formation 3D MHD simulations (Anaïs Pauchet)
Protostellar Outflows: From Simulations to Synthetic Observations (Taishi Ushirogi)