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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2212.08585 |
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| _version_ | 1866913252468850688 |
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| author | Rawash, Sami Turton, David |
| author_facet | Rawash, Sami Turton, David |
| contents | Large classes of multi-center supergravity solutions have been constructed in the study of supersymmetric black holes and their microstates. Many smooth multi-center solutions have the same charges as supersymmetric black holes, with all centers deep inside a long black-hole-like throat. These configurations are constrained by regularity, absence of closed timelike curves, and charge quantization. Due to these constraints, constructing explicit solutions with several centers in generic arrangements, and with all parameters in physically relevant ranges, is a hard task. In this work we present an optimization algorithm, based on evolutionary algorithms and Bayesian optimization, that systematically constructs numerical solutions satisfying all constraints. We exhibit explicit examples of novel five-center and seven-center machine-precision solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_08585 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Evolutionary algorithms for multi-center solutions Rawash, Sami Turton, David High Energy Physics - Theory General Relativity and Quantum Cosmology Computational Physics Large classes of multi-center supergravity solutions have been constructed in the study of supersymmetric black holes and their microstates. Many smooth multi-center solutions have the same charges as supersymmetric black holes, with all centers deep inside a long black-hole-like throat. These configurations are constrained by regularity, absence of closed timelike curves, and charge quantization. Due to these constraints, constructing explicit solutions with several centers in generic arrangements, and with all parameters in physically relevant ranges, is a hard task. In this work we present an optimization algorithm, based on evolutionary algorithms and Bayesian optimization, that systematically constructs numerical solutions satisfying all constraints. We exhibit explicit examples of novel five-center and seven-center machine-precision solutions. |
| title | Evolutionary algorithms for multi-center solutions |
| topic | High Energy Physics - Theory General Relativity and Quantum Cosmology Computational Physics |
| url | https://arxiv.org/abs/2212.08585 |