Conditional MAFs interpolate QCD chiral phase structure across coupling, mass, and volume, reproducing reweighting while cutting required ensembles despite bias near transitions.
Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory
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abstract
We present a procedure for reconstructing the decision function of an artificial neural network as a simple function of the input, provided the decision function is sufficiently symmetric. In this case one can easily deduce the quantity by which the neural network classifies the input. The procedure is embedded into a pipeline of machine learning algorithms able to detect the existence of different phases of matter, to determine the position of phase transitions and to find explicit expressions of the physical quantities by which the algorithm distinguishes between phases. We assume no prior knowledge about the Hamiltonian or the order parameters except Monte Carlo-sampled configurations. The method is applied to the Ising Model and SU(2) lattice gauge theory. In both systems we deduce the explicit expressions of the known order parameters from the decision functions of the neural networks.
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A neural network trained on 2D SU(2) lattices with inserted thin Z2 vortices, after random gauge transformations, noise, and cooling, can locate center vortices at moderate visibility levels and scales via tiling.
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Testing machine-learned distributions against Monte Carlo data for the QCD chiral phase transition
Conditional MAFs interpolate QCD chiral phase structure across coupling, mass, and volume, reproducing reweighting while cutting required ensembles despite bias near transitions.
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First steps towards gauge-independent vortex identification through machine learning
A neural network trained on 2D SU(2) lattices with inserted thin Z2 vortices, after random gauge transformations, noise, and cooling, can locate center vortices at moderate visibility levels and scales via tiling.