Characterizing 4-string contact interaction using machine learning
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866911828106280960 |
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| author | Erbin, Harold Fırat, Atakan Hilmi |
| author_facet | Erbin, Harold Fırat, Atakan Hilmi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_09129 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Characterizing 4-string contact interaction using machine learning Erbin, Harold Fırat, Atakan Hilmi High Energy Physics - Theory Machine Learning Complex Variables 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. |
| title | Characterizing 4-string contact interaction using machine learning |
| topic | High Energy Physics - Theory Machine Learning Complex Variables |
| url | https://arxiv.org/abs/2211.09129 |