Characterizing 4-string contact interaction using machine learning. (arXiv:2211.09129v1 [hep-th])

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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