Modeling blazar broadband emission with convolutional neural networks -- II. External Compton model
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929240869437440 |
|---|---|
| author | Sahakyan, N. Bégué, D. Casotto, A. Dereli-Bégué, H. Giommi, P. Gasparyan, S. Vardanyan, V. Khachatryan, M. Pe'er, A. |
| author_facet | Sahakyan, N. Bégué, D. Casotto, A. Dereli-Bégué, H. Giommi, P. Gasparyan, S. Vardanyan, V. Khachatryan, M. Pe'er, A. |
| contents | In the context of modeling spectral energy distributions (SEDs) for blazars, we extend the method that uses a convolutional neural network (CNN) to include external inverse Compton processes. The model assumes that relativistic electrons within the emitting region can interact and up-scatter external photon originating from the accretion disk, the broad-line region, and the torus, to produce the observed high-energy emission. We trained the CNN on a numerical model that accounts for the injection of electrons, their self-consistent cooling, and pair creation-annihilation processes, considering both internal and all external photon fields. Despite the larger number of parameters compared to the synchrotron self-Compton model and the greater diversity in spectral shapes, the CNN enables an accurate computation of the SED for a specified set of parameters. The performance of the CNN is demonstrated by fitting the SED of two flat-spectrum radio quasars, namely 3C 454.3 and CTA 102, and obtaining their parameter posterior distributions. For the first source, the available data in the low-energy band allowed us to constrain the minimum Lorentz factor of the electrons, $γ_{\rm min}$, while for the second source, due to the lack of these data, $γ_{\rm min} = 10^2$ was set. We used the obtained parameters to investigate the energetics of the system. The model developed here, along with one from Bégué et al. (2023), enables self-consistent, in-depth modeling of blazar broadband emissions within leptonic scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07495 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling blazar broadband emission with convolutional neural networks -- II. External Compton model Sahakyan, N. Bégué, D. Casotto, A. Dereli-Bégué, H. Giommi, P. Gasparyan, S. Vardanyan, V. Khachatryan, M. Pe'er, A. High Energy Astrophysical Phenomena In the context of modeling spectral energy distributions (SEDs) for blazars, we extend the method that uses a convolutional neural network (CNN) to include external inverse Compton processes. The model assumes that relativistic electrons within the emitting region can interact and up-scatter external photon originating from the accretion disk, the broad-line region, and the torus, to produce the observed high-energy emission. We trained the CNN on a numerical model that accounts for the injection of electrons, their self-consistent cooling, and pair creation-annihilation processes, considering both internal and all external photon fields. Despite the larger number of parameters compared to the synchrotron self-Compton model and the greater diversity in spectral shapes, the CNN enables an accurate computation of the SED for a specified set of parameters. The performance of the CNN is demonstrated by fitting the SED of two flat-spectrum radio quasars, namely 3C 454.3 and CTA 102, and obtaining their parameter posterior distributions. For the first source, the available data in the low-energy band allowed us to constrain the minimum Lorentz factor of the electrons, $γ_{\rm min}$, while for the second source, due to the lack of these data, $γ_{\rm min} = 10^2$ was set. We used the obtained parameters to investigate the energetics of the system. The model developed here, along with one from Bégué et al. (2023), enables self-consistent, in-depth modeling of blazar broadband emissions within leptonic scenario. |
| title | Modeling blazar broadband emission with convolutional neural networks -- II. External Compton model |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2402.07495 |