A framework for modeling the evolution of young stellar objects
arXiv:2507.16944 · doi:10.3847/1538-4357/ade99d
Abstract
Measuring properties of young stellar objects (YSOs) is necessary for probing the pre-main-sequence evolution of stars. As YSOs exhibit complex geometry, measurement generally entails comparing observed radiation to template populations of radiative-transfer model YSO spectral energy distributions (SEDs). Due to uncertainty on the precise mechanics of star formation, the properties inferred for YSOs using these models often depend strongly on the assumed accretion history. We develop a framework for predicting observable properties of YSOs that is agnostic to the underlying accretion history, enabling comparison between theories. This framework links a set of radiative-transfer SEDs with protostellar evolutionary tracks to create models of evolving YSOs. Unlike previous works, we directly relate evolution models to observables through theoretical physical parameters rather than through intermediate, observationally derived analogues. We make flux predictions for YSOs corresponding to stars with birth masses from 0.2 to 50 during their accretion phase following isothermal-sphere, turbulent-core, and competitive accretion histories, showing that these histories may be observationally distinguished by examining the 100-m and 3-mm fluxes of a YSO. We discuss the impact of dust models and parameter ranges on the output of radiative transfer simulations through a comparison to another SED model grid. We quantify the degree of confusion between YSO Stages and Classes across a wide range of physical scenarios; for each, we calculate confusion matrices that enable inference of the number of objects of a given Stage from an observed population. Finally, we critically examine the physical significance of various literature Stage and Class definitions.
36 pages, 18 figures, 1 table. Includes appendix: 6 pages, 5 figures. To be published in ApJ. Confusion matrices available at https://doi.org/10.5281/zenodo.13922040
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