Contribution to a conference proceedings

FZJ-2022-06015

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Error reduction using machine learning on Ising worm simulation

Kim, J. (Corresponding author)FZJ* ; Unger, W.

2022

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Please use a persistent id in citations: http://hdl.handle.net/2128/33235

Abstract: 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.

Note: 8 pages, 18 figures, 39th International Symposium on Lattice Field Theory, LATTICE2022 8th-13th August 2022, Bonn, Germany

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Contributing Institute(s):

1. Theorie der Starken Wechselwirkung (IAS-4)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2022

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