Linking simulations and observations of star-forming regions I. Masses and temperatures of ALMAGAL-like cores

Abstract

Dense cores and clumps in massive star-forming regions are studied through (sub-)millimetre continuum observations, but inferred masses and temperatures can be biased by limited resolution, interferometric filtering, projection, and uncertain dust temperatures. We quantify how reliably core masses and temperatures can be inferred from ALMA-like continuum data using synthetic observations of such clumps. We post-processed radiation-hydrodynamic simulations of collapsing 1000 M$_\odot$ clumps with RADMC-3D and CASA to generate synthetic ALMAGAL-like images. Cores were defined around sink particles, enabling comparison of masses from the 3D simulations, idealised RADMC-3D maps, and synthetic ALMA images. We quantified how resolution, projection, and viewing angle affect core masses, and tested three dust-temperature estimators (clump-scale L/M, a L/M+flux model, and a supervised machine-learning estimate). Projection effects cause line-of-sight integrated core masses within 1500 au to exceed spherical masses by factors of two to three. The empirical L/M temperature prescription follows the global thermal evolution but misses the core-to-core scatter, producing core-mass errors of a factor of a few. Including local flux improves temperature and mass estimates, while a gradient-boosting machine-learning model suggests further improvement. Synthetic ALMAGAL-like images recover the global core structure, but flux-reconstruction uncertainties introduce mass errors up to a factor of three and limit reliable core-mass recovery to $\gtrsim 0.3$ M$_\odot$, comparable to the ALMAGAL completeness limit. Individual core masses are typically uncertain by a factor of three to five, mainly due to dust-temperature assumptions and flux reconstruction. Reducing these biases requires temperature estimates that include local core information rather than a single clump-scale L/M-based prescription.