PoS - Proceedings of Science
Volume 430 - The 39th International Symposium on Lattice Field Theory (LATTICE2022) - Algorithms
Error reduction using machine learning on Ising worm simulation
J. Kim* and W. Unger
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Pre-published on: January 27, 2023
Published on: April 06, 2023
We develop a method to improve on the statistical errors for higher moments using machine learning techniques. We present here results for the dual representation of the Ising model with an external field, derived via the high temperature expansion and simulated by the worm algorithm. We compare two ways of measuring the same set of observables, without and with machine learning: moments of the magnetization and the susceptibility can be improved by using the decision tree method to train the correlations between the higher moments and the second moment obtained from an integrated 2-point function. Those results are compared in small volumes to analytic predictions.
DOI: https://doi.org/10.22323/1.430.0018
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