Characterizing 4-string contact interaction using machine learning

Abstract <p>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 ofn-string contact interaction is feasible.</p>. Authors: Erbin, Harold (ORCID:0000000290960659); Fırat, Atakan (ORCID:0000000273067367). DOE Contract: SC0012567. Subjects: Physics