BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//adass2023//speaker//FGL7PQ
BEGIN:VTIMEZONE
TZID:US/Arizona
BEGIN:STANDARD
DTSTART:20221106T000000
TZNAME:MST
TZOFFSETFROM:-0700
TZOFFSETTO:-0700
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
SUMMARY:Predicting the Radiation Field of Molecular Clouds using Denoising
  Diffusion Probabilistic Models - Duo Xu
DTSTART;TZID=US/Arizona:20231106T083000
DTEND;TZID=US/Arizona:20231106T083000
DTSTAMP:20260911T191953Z
UID:pretalx-adass2023-AZDQJY@pretalx.com
DESCRIPTION:Accurately quantifying the impact of radiation feedback in sta
 r formation is challenging. To address this complex problem\, we employ de
 ep learning techniques\, denoising diffusion probabilistic models (DDPMs)
 \, to predict the interstellar radiation field (ISRF) strength based on th
 ree-band dust emission at 4.5 \\um\, 24 \\um\, and 250 \\um. We adopt magn
 etohydrodynamic 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 observe
 d spectral energy distributions in the Monoceros R2 (MonR2) GMC. We train 
 DDPMs to estimate the ISRF using synthetic three-band dust emission. The d
 ispersion between the predictions and true values is within a factor of 0.
 1 for the test set. We further evaluate the diffusion model's performance 
 on new simulations with ISRF intensities 10 and 100 times higher than that
  of the fiducial simulations. Despite a systematic underestimation factor 
 of 1.8 and 2.7 for the higher ISRF simulations\, the relative intensity re
 mains well constrained. 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 cor
 respondence between intense ISRF\, bright sources\, and high dust emission
 \, confirming the model's ability to capture ISRF variations. Our model pr
 ovides a robust means to predict the distribution of radiation feedback ev
 en where the ISRF is complex and not well constrained\, such as in regions
  influenced by nearby star clusters.
LOCATION:Posters
URL:https://pretalx.com/adass2023/talk/AZDQJY/
END:VEVENT
END:VCALENDAR