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Characterizing 4-string contact interaction using machine learning

Harold Erbin
(
- MIT, Cambridge, CTP and
- IAIFI, Cambridge and
- LIST, Saclay
)
,
Atakan Hilmi Fırat
(
- MIT, Cambridge, CTP and
- IAIFI, Cambridge
)

Nov 16, 2022

38 pages

Published in:

JHEP 04 (2024) 016

Published: Apr 3, 2024

e-Print:

2211.09129 [hep-th]

DOI:

10.1007/JHEP04(2024)016

Report number:

MIT-CTP/5494

View in:

reference search23 citations

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Abstract: (Springer)

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.

Note:

28+10 pages, 13 figures, 6 tables

String Field Theory
Differential and Algebraic Geometry
Bosonic Strings
field theory: string
string: closed
tachyon: potential
contact interaction
geometry
machine learning
neural network

References(87)

Figures(27)

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