Characterizing 4-string contact interaction using machine learning
The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning. We obtain Strebel quadratic differentials on 4-punctured spheres as a neural network by performing unsupervised learning with a custom-built loss function. This allows us to solve for...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2024
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| Online Access: | https://hdl.handle.net/1721.1/154088 |
| _version_ | 1824458381491961856 |
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| author | Erbin, Harold Fırat, Atakan Hilmi |
| author2 | Massachusetts Institute of Technology. Center for Theoretical Physics |
| author_facet | Massachusetts Institute of Technology. Center for Theoretical Physics Erbin, Harold Fırat, Atakan Hilmi |
| author_sort | Erbin, Harold |
| collection | MIT |
| description | The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning. We obtain Strebel quadratic differentials on 4-punctured spheres as a neural network by performing unsupervised learning with a custom-built loss function. This allows us to solve for local coordinates and compute their associated mapping radii numerically. We also train a neural network distinguishing vertex from Feynman region. As a check, 4-tachyon contact term in the tachyon potential is computed and a good agreement with the results in the literature is observed. We argue that our algorithm is manifestly independent of number of punctures and scaling it to characterize the geometry of n-string contact interaction is feasible. |
| first_indexed | 2024-09-23T15:55:29Z |
| format | Article |
| id | mit-1721.1/154088 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:24:59Z |
| publishDate | 2024 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1540882025-01-04T04:14:10Z Characterizing 4-string contact interaction using machine learning Erbin, Harold Fırat, Atakan Hilmi Massachusetts Institute of Technology. Center for Theoretical Physics Nuclear and High Energy Physics The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning. We obtain Strebel quadratic differentials on 4-punctured spheres as a neural network by performing unsupervised learning with a custom-built loss function. This allows us to solve for local coordinates and compute their associated mapping radii numerically. We also train a neural network distinguishing vertex from Feynman region. As a check, 4-tachyon contact term in the tachyon potential is computed and a good agreement with the results in the literature is observed. We argue that our algorithm is manifestly independent of number of punctures and scaling it to characterize the geometry of n-string contact interaction is feasible. 2024-04-08T14:14:49Z 2024-04-08T14:14:49Z 2024-04-03 2024-04-07T03:11:27Z Article http://purl.org/eprint/type/JournalArticle 1029-8479 https://hdl.handle.net/1721.1/154088 Journal of High Energy Physics. 2024 Apr 03;2024(4):16 PUBLISHER_CC en 10.1007/jhep04(2024)016 Journal of High Energy Physics Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Science and Business Media LLC Springer Berlin Heidelberg |
| spellingShingle | Nuclear and High Energy Physics Erbin, Harold Fırat, Atakan Hilmi Characterizing 4-string contact interaction using machine learning |
| title | Characterizing 4-string contact interaction using machine learning |
| title_full | Characterizing 4-string contact interaction using machine learning |
| title_fullStr | Characterizing 4-string contact interaction using machine learning |
| title_full_unstemmed | Characterizing 4-string contact interaction using machine learning |
| title_short | Characterizing 4-string contact interaction using machine learning |
| title_sort | characterizing 4 string contact interaction using machine learning |
| topic | Nuclear and High Energy Physics |
| url | https://hdl.handle.net/1721.1/154088 |
| work_keys_str_mv | AT erbinharold characterizing4stringcontactinteractionusingmachinelearning AT fıratatakanhilmi characterizing4stringcontactinteractionusingmachinelearning |