Learning Parameters of dRGT Black Holes in Nonlinear Electrodynamics with Neural Network Scenarios

@misc{belhaj2026learning,
  title = {{Learning Parameters of dRGT Black Holes in Nonlinear Electrodynamics with Neural Network Scenarios}},
  author = {Adil Belhaj and Salah Eddine Ennadifi and Maryem Jemri},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.28684},
  eprint = {2610.28684},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.28684},
}

TY  - UNPB
TI  - Learning Parameters of dRGT Black Holes in Nonlinear Electrodynamics with Neural Network Scenarios
AU  - Adil Belhaj
AU  - Salah Eddine Ennadifi
AU  - Maryem Jemri
PY  - 2026
DA  - 2026/10/19
PB  - arXiv
DO  - 10.48550/arxiv.2610.28684
UR  - https://arxiv.org/abs/2610.28684
AB  - Using machine learning methods, we present constraining parameter scenarios of AdS black holes in dRGT-like massive gravity coupled to nonlinear electrodynamics. We first approach the photon sphere, the black hole shadow, and the energy emission rate with the help of high performance parallel computations. To corroborate such optical results, we develop a numerical framework to constrain the electric charge and the nonlinear electrodynamic parameter, for certain gravity regimes, via the shadow observations reported by the Even Horizon Telescope collaboration. Employing neural networks, we then examine the parameter regions of the black holes based on their consistency with EHT observations of M87^*, SgrA^*_VLTI, and SgrA^*_Keck, within the 1−σ and 2−σ confidence intervals. As a result, we find that the obtained constraints are consistent with the SgrA^*_VLTI empirical data.
ER  -