Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.05549 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909605781569536 |
|---|---|
| author | Jejjala, Vishnu Nampuri, Suresh Nxumalo, Dumisani Roy, Pratik Swain, Abinash |
| author_facet | Jejjala, Vishnu Nampuri, Suresh Nxumalo, Dumisani Roy, Pratik Swain, Abinash |
| contents | Modular, Jacobi, and mock-modular forms serve as generating functions for BPS black hole degeneracies. By training feed-forward neural networks on Fourier coefficients of automorphic forms derived from the Dedekind eta function, Eisenstein series, and Jacobi theta functions, we demonstrate that machine learning techniques can accurately predict modular weights from truncated expansions. Our results reveal strong performance for negative weight modular and quasi-modular forms, particularly those arising in exact black hole counting formulae, with lower accuracy for positive weights and more complicated combinations of Jacobi theta functions. This study establishes a proof of concept for using machine learning to identify how data is organized in terms of modular symmetries in gravitational systems and suggests a pathway toward automated detection and verification of symmetries in quantum gravity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05549 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning automorphic forms for black holes Jejjala, Vishnu Nampuri, Suresh Nxumalo, Dumisani Roy, Pratik Swain, Abinash High Energy Physics - Theory Machine Learning Number Theory Modular, Jacobi, and mock-modular forms serve as generating functions for BPS black hole degeneracies. By training feed-forward neural networks on Fourier coefficients of automorphic forms derived from the Dedekind eta function, Eisenstein series, and Jacobi theta functions, we demonstrate that machine learning techniques can accurately predict modular weights from truncated expansions. Our results reveal strong performance for negative weight modular and quasi-modular forms, particularly those arising in exact black hole counting formulae, with lower accuracy for positive weights and more complicated combinations of Jacobi theta functions. This study establishes a proof of concept for using machine learning to identify how data is organized in terms of modular symmetries in gravitational systems and suggests a pathway toward automated detection and verification of symmetries in quantum gravity. |
| title | Machine learning automorphic forms for black holes |
| topic | High Energy Physics - Theory Machine Learning Number Theory |
| url | https://arxiv.org/abs/2505.05549 |