Characterizing 4-string contact interaction using machine learning
arXiv:2211.09129 · doi:10.1007/JHEP04(2024)016
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 -string contact interaction is feasible.
arXiv comment28+10 pages, 13 figures, 6 tables
References in corpus (46)
- Closed String Field Theory: Quantum Action and the BV Master Equation
- Introduction to string field theory
- Tachyon Condensation on the Brane Antibrane System
- Universality of the Tachyon Potential
- Nonparametric regression using deep neural networks with ReLU activation function
- Tachyon condensation in string field theory
- Tachyon Condensation in Superstring Field Theory
- D-branes as Tachyon Lumps in String Field Theory
- Level truncation and the tachyon in open bosonic string field theory
- Closed Superstring Field Theory and its Applications
- From Free Fields to -- III
- Evolving neural networks with genetic algorithms to study the String Landscape
- A Closed String Tachyon Vacuum ?
- Experimental String Field Theory
- Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
- Large Marginal Deformations in String Field Theory
- Neural Networks and Quantum Field Theory
- Twisted Tachyon Condensation in Closed String Field Theory
- Four Lectures on Closed String Field Theory
- String Field Theory -- A Modern Introduction
- Rolling Closed String Tachyons and the Big Crunch
- Precision Machine Learning
- A perturbative analysis of tachyon condensation
- Off-shell Closed String Amplitudes: Towards a Computation of the Tachyon Potential
- From Symmetric Product CFTs to
- Nonperturbative renormalization for the neural network-QFT correspondence
- Hyperbolic three-string vertex
- Open-closed Hyperbolic String Vertices
- Effective Tachyonic Potential in Closed String Field Theory
- Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
- Closed Bosonic String Field Theory At Quartic Order
- Closed Bosonic String Field Theory at Quintic Order: Five-Tachyon Contact Term and Dilaton Theorem
- Explore and Exploit with Heterotic Line Bundle Models
- Testing Closed String Field Theory with Marginal Fields
- Minimal-area metrics on the Swiss cross and punctured torus
- Inception Neural Network for Complete Intersection Calabi-Yau 3-folds
- Closed Bosonic String Field Theory at Quintic Order II: Marginal Deformations and Effective Potential
- Dilaton Deformations in Closed String Field Theory
- Moduli-dependent KK towers and the swampland distance conjecture on the quintic Calabi-Yau manifold
- A tachyon lump in closed string field theory
- Calabi-Yau Metrics, Energy Functionals and Machine-Learning
- Deep multi-task mining Calabi-Yau four-folds
- The nonperturbative closed string tachyon vacuum to high level
- Twistor Coverings and Feynman Diagrams
- Connections between reflected entropies and hyperbolic string vertices
- Classical geometry from the tensionless string