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Michael Grudic

Publications and source records attributed to Michael Grudic.

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Assessing Zeeman Measurements of Magnetic Fields in Synthetic HI Observations

Zeeman observations provide the only direct probe of line-of-sight (LOS) magnetic fields in the interstellar medium. To evaluate their accuracy and limitations, we generate synthetic HI Zeeman spectra from magnetohydrodynamic simulations and idealized cloud models, and analyze the resulting Stokes I and V profiles using two complementary methods. Approach I uses the classical relation between Stokes V and dI/d{\nu} to estimate LOS-averaged magnetic fields, achieving an upper-limit relative error of 16% (half-width of 68.27% confidence interval) for a representative noise level of 0.014 K. Approach II applies Gaussian decomposition to Stokes I and V to estimate component-level magnetic fields, yielding a 13% relative error quantifying the same confidence range, reflecting the intrinsic uncertainty of such Zeeman estimates. Both approaches recover the original fields under uniform-field conditions and remain robust in turbulent environments. Approach I provides a simple and reliable LOS-averaged field estimate, while Approach II, although more complex, offers statistical insight into magnetic field variations along the LOS. We further show that joint fitting of Stokes I and V generally outperforms sequential fitting, particularly in the presence of attenuation. Increasing noise eight-fold produces a more modest rise in uncertainty, doubling to a 26% relative error, while substantial optical depth introduces only a minor additional contribution to the overall uncertainty. Applying these methods to FAST observations of the L1544 star-forming region, we confirm the previously reported LOS magnetic field strength, demonstrating the validity of Zeeman analysis in this benchmark core.

astro-ph.GA

Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models

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.

astro-ph.GA