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Abstract Most simulations of galaxies and massive giant molecular clouds (GMCs) cannot explicitly resolve the formation (or predict the main-sequence masses) of individual stars. So they must use some prescription for the amount of feedback from an assumed population of massive stars (e.g. sampling the initial mass function, IMF). We perform a methods study of simulations of a star-forming GMC with stellar feedback from UV radiation, varying only the prescription for determining the luminosity of each stellar mass element formed (according to different IMF sampling schemes). We show that different prescriptions can lead to widely varying (factor of ∼3) star formation efficiencies (on GMC scales) even though the average mass-to-light ratios agree. Discreteness of sources is important: radiative feedback from fewer, more-luminous sources has a greater effect for a given total luminosity. These differences can dominate over other, more widely recognized differences between similar literature GMC-scale studies (e.g. numerical methods, cloud initial conditions, presence of magnetic fields). Moreover the differences in these methods are not purely numerical: some make different implicit assumptions about the nature of massive star formation, and this remains deeply uncertain in star formation theory.
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Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Abstract Accurately quantifying the impact of radiation feedback in star formation is challenging. To address this complex problem, we employ deep-learning techniques known as denoising diffusion probabilistic models (DDPMs) to predict the interstellar radiation field (ISRF) strength based on three-band dust emission at 4.5, 24, and 250μm. We adopt magnetohydrodynamic simulations from the STARFORGE project that model star formation and giant molecular cloud (GMC) evolution. We generate synthetic dust emission maps matching observed spectral energy distributions in the Monoceros R2 (MonR2) GMC. We train DDPMs to estimate the ISRF using synthetic three-band dust emission. The dispersion between the predictions and true values is within a factor of 0.1 for the test set. We extended our assessment of the diffusion model to include new simulations with varying physical parameters. While there is a consistent offset observed in these out-of-distribution simulations, the model effectively constrains the relative intensity to within a factor of 2. Meanwhile, our analysis reveals a weak correlation between the ISRF solely derived from dust temperature and the actual ISRF. We apply our trained model to predict the ISRF in MonR2, revealing a correspondence between intense ISRF, bright sources, and high dust emission, confirming the model’s ability to capture ISRF variations. Our model robustly predicts radiation feedback distribution, even in complex, poorly constrained ISRF environments like those influenced by nearby star clusters. However, precise ISRF predictions require an accurate training data set mirroring the target molecular cloud’s unique physical conditions.
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- PAR ID:
- 10533107
- Publisher / Repository:
- ApJ
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 958
- Issue:
- 1
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 97
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3847/1538-4357/acfedc
