Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.11767 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915392000098304 |
|---|---|
| author | Chen, Xingzhuo Braga-Neto, Ulisses Wang, Lifan Kasen, Daniel Liu, Zhengwei Roepke, F. K. Zhong, Ming Jeffery, David J. |
| author_facet | Chen, Xingzhuo Braga-Neto, Ulisses Wang, Lifan Kasen, Daniel Liu, Zhengwei Roepke, F. K. Zhong, Ming Jeffery, David J. |
| contents | We present SEDONA-GesaRaT, a rapid code for supernova radiative transfer simulation developed based on the Monte-Carlo radiative transfer code SEDONA. We use a set of atomic physics neural networks (APNN), an artificial intelligence (AI) solver for the non-local thermodynamic equilibrium (NLTE) atomic physics level population calculation, which is trained and validated on 119 1-D type Ia supernova (SN Ia) radiative transfer simulation results showing great computation speed and accuracy. SEDONA-GesaRaT has been applied to the 3-D SN Ia explosion model N100 to perform a 3-D NLTE radiative transfer calculation. The spatially resolved linear polarization data cubes of the N100 model are successfully retrieved with a high signal-to-noise ratio using the integral-based technique (IBT). The overall computation cost of a 3-D NLTE spectropolarimetry simulation using SEDONA-GesaRaT is only $\sim$3000 core-hours, while the previous codes could only finish 1-D NLTE simulation, or 3-D local thermodynamic equilibrium (LTE) simulation, with similar computation resources. The excellent computing efficiency allows SEDONA-GesaRaT for future large-scale simulations that systematically study the internal structures of supernovae. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SEDONA-GesaRaT: an AI-Accelerated Radiative Transfer Program for 3-D Supernova Simulations Chen, Xingzhuo Braga-Neto, Ulisses Wang, Lifan Kasen, Daniel Liu, Zhengwei Roepke, F. K. Zhong, Ming Jeffery, David J. High Energy Astrophysical Phenomena We present SEDONA-GesaRaT, a rapid code for supernova radiative transfer simulation developed based on the Monte-Carlo radiative transfer code SEDONA. We use a set of atomic physics neural networks (APNN), an artificial intelligence (AI) solver for the non-local thermodynamic equilibrium (NLTE) atomic physics level population calculation, which is trained and validated on 119 1-D type Ia supernova (SN Ia) radiative transfer simulation results showing great computation speed and accuracy. SEDONA-GesaRaT has been applied to the 3-D SN Ia explosion model N100 to perform a 3-D NLTE radiative transfer calculation. The spatially resolved linear polarization data cubes of the N100 model are successfully retrieved with a high signal-to-noise ratio using the integral-based technique (IBT). The overall computation cost of a 3-D NLTE spectropolarimetry simulation using SEDONA-GesaRaT is only $\sim$3000 core-hours, while the previous codes could only finish 1-D NLTE simulation, or 3-D local thermodynamic equilibrium (LTE) simulation, with similar computation resources. The excellent computing efficiency allows SEDONA-GesaRaT for future large-scale simulations that systematically study the internal structures of supernovae. |
| title | SEDONA-GesaRaT: an AI-Accelerated Radiative Transfer Program for 3-D Supernova Simulations |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2507.11767 |