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Characterizing 4-string contact interaction using machine learning

arXiv 2211.09129 ↗First posted 16 Nov 2022hep-th cs.LG math.CVcited by 0

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Abstract (arXiv)

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.

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23 of 90 references.

References · 23

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11 more
  1. 2021Building Quantum Field Theories Out of Neuronscited by 27
  2. 2021Nonperturbative renormalization for the neural network-QFT correspondencecited by 24
  3. 2018Complex-valued Neural Networks with Non-parametric Activation Functionscited by 19
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  7. 2020Explore and Exploit with Heterotic Line Bundle Modelscited by 11
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  9. 2022Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?cited by 10
  10. 2021Calabi-Yau Metrics, Energy Functionals and Machine-Learningcited by 9
  11. 2022Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learningcited by 2

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