Characterizing 4-string contact interaction using machine learning
Name
13130_2024_Article_23140.pdf
Size
13.07 MB
Format
Adobe PDF
Checksum (MD5)
e42715a33f81bf11a7bea821dd05c428
Author(s)
Erbin, Harold
Fırat, Atakan Hilmi
Date Issued
April 3, 2024
Journal
Journal of High Energy Physics
Publisher
Springer Science and Business Media LLC
Citation
Journal of High Energy Physics. 2024 Apr 03;2024(4):16
Version
Final published version
Abstract
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.
Subjects
Nuclear and High Energy Physics
MIT Department
Massachusetts Institute of Technology. Center for Theoretical Physics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/jhep04(2024)016
