Characterizing 4-string contact interaction using machine learning

Harold Erbin

doi:10.1007/JHEP04(2024)016

Characterizing 4-string contact interaction using machine learning

2024-04-03

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DOI：10.1007/JHEP04(2024)016

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Abstract

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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.

Keywords:

String Field Theory

Differential and Algebraic Geometry

Bosonic Strings

Characterizing 4-string contact interaction using machine learning

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