×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models. (arXiv:2309.05811v1 [astro-ph.GA])

Click here to flash read.

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.

Click here to read this post out
ID: 395562; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: Sept. 13, 2023, 7:30 a.m. Changes:
/u/anonymous
Dictionaries:
Words:
Spaces:
Views: 12
CC:
No creative common's license
Comments: