Constraining Black Hole Parameters in Non-Commutative Geometry using Machine Learning
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866916037767725056 |
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| author | Jemri, Maryem |
| author_facet | Jemri, Maryem |
| contents | Motivated by string theory, we constrain non-commutative black hole parameters through shadow behaviors using machine learning techniques combined by CUDA computations. To do so, we first investigate the structure of the event horizon of non-commutative black holes in the presence of string clouds and dark energy sectors by exploiting CUDA-based methods. We numerically approach the shadow properties and the energy emission rate of rotating and charged black holes in non-commutative geometry via such high-performance parallel computings. To bridge these findings with observational data, we implement a CUDA-based framework in order to constrain the involved black hole parameters including the non-commutative one. Using the resulting numerical data, we build a robust training datasets for a fully connected neural network to determine whether a given set of parameters matches with the observational data provided by Event Horizon Telescope collaborations. As a result, we find that the non-commutative model under study is consistent with the observations of $SgrA^*_{\mathrm{Keck}}$ black holes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22862 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Constraining Black Hole Parameters in Non-Commutative Geometry using Machine Learning Jemri, Maryem General Relativity and Quantum Cosmology High Energy Physics - Theory Motivated by string theory, we constrain non-commutative black hole parameters through shadow behaviors using machine learning techniques combined by CUDA computations. To do so, we first investigate the structure of the event horizon of non-commutative black holes in the presence of string clouds and dark energy sectors by exploiting CUDA-based methods. We numerically approach the shadow properties and the energy emission rate of rotating and charged black holes in non-commutative geometry via such high-performance parallel computings. To bridge these findings with observational data, we implement a CUDA-based framework in order to constrain the involved black hole parameters including the non-commutative one. Using the resulting numerical data, we build a robust training datasets for a fully connected neural network to determine whether a given set of parameters matches with the observational data provided by Event Horizon Telescope collaborations. As a result, we find that the non-commutative model under study is consistent with the observations of $SgrA^*_{\mathrm{Keck}}$ black holes. |
| title | Constraining Black Hole Parameters in Non-Commutative Geometry using Machine Learning |
| topic | General Relativity and Quantum Cosmology High Energy Physics - Theory |
| url | https://arxiv.org/abs/2605.22862 |