Accurately quantifying the impact of radiation feedback in star formation is challenging. To address this complex problem, we employ deep learning techniques, denoising diffusion probabilistic models (DDPMs), to predict the interstellar radiation field (ISRF) strength based on three-band dust emission at 4.5 \um, 24 \um, and 250 \um. We adopt magnetohydrodynamic simulations from the STARFORGE (STAR FORmation in Gaseous Environments) 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 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 dataset mirroring the target molecular cloud's unique physical conditions.

Stars are formed by molecular gas and dust coalescing in space. These molecular gases are so dilute and cold that they are invisible to the human eye, but they do emit faint radio waves that can be observed by radio telescopes. Observing from Earth, a lot of matter lies ahead and behind these molecular clouds, and these overlapping features make it difficult to determine their distance and physical properties, such as size and mass. So, even though our galaxy, the Milky Way, is the only galaxy close enough to make detailed observations of molecular clouds in the universe, it has been very difficult to investigate the physical properties of molecular clouds in a cohesive manner from large-scale observations. A research team led by Dr. Shinji Fujita from the Osaka Metropolitan University Graduate School of Science, identified about 140,000 molecular clouds in the Milky Way galaxy, which are areas of star formation, from large-scale data of carbon monoxide molecules, observed in detail by the Nobeyama 45-m radio telescope.

Artificial intelligence can make it possible to see astrophysical phenomena that were previously beyond reach. This has now been demonstrated by scientists from the CNRS, IRAM, Observatoire de Paris-PSL, Ecole Centrale Marseille and Ecole Centrale Lille, working together in the ORION-B program. In a series of three papers published in Astronomy & Astrophysics on 19 November 2020, they present the most comprehensive observations yet carried out of one of the star-forming regions closest to the Earth. The gas clouds in which stars are born and evolve are vast regions that are extremely rich in matter, and hence in physical processes. All these processes are intertwined at different size and time scales, making it almost impossible to fully understand such stellar nurseries.