Flow-based density of states for complex actions
arXiv:2203.01243 · doi:10.1103/PhysRevD.108.054511
Abstract
Emerging sampling algorithms based on normalizing flows have the potential to solve ergodicity problems in lattice calculations. Furthermore, it has been noted that flows can be used to compute thermodynamic quantities which are difficult to access with traditional methods. This suggests that they are also applicable to the density-of-states approach to complex action problems. In particular, flow-based sampling may be used to compute the density directly, in contradistinction to the conventional strategy of reconstructing it via measuring and integrating the derivative of its logarithm. By circumventing this procedure, the accumulation of errors from the numerical integration is avoided completely and the overall normalization factor can be determined explicitly. In this proof-of-principle study, we demonstrate our method in the context of two-component scalar field theory where the symmetry is explicitly broken by an imaginary external field. First, we concentrate on the zero-dimensional case which can be solved exactly. We show that with our method, the Lee-Yang zeroes of the associated partition function can be successfully located. Subsequently, we confirm that the flow-based approach correctly reproduces the density computed with conventional methods in one- and two-dimensional models.
9 pages, 4 figures
References in corpus (18)
- Equivariant flow-based sampling for lattice gauge theory
- Can stochastic quantization evade the sign problem? -- the relativistic Bose gas at finite chemical potential
- Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
- Sampling using gauge equivariant flows
- The density of states in gauge theories
- The density of states approach to dense quantum systems
- Efficient Modelling of Trivializing Maps for Lattice Theory Using Normalizing Flows: A First Look at Scalability
- Flow-based sampling for fermionic lattice field theories
- Stochastic normalizing flows as non-equilibrium transformations
- Flow-based sampling in the lattice Schwinger model at criticality
- Normalizing Flows and the Real-Time Sign Problem
- Topological susceptibility of pure gauge theory using Density of States
- Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
- Mitigating the Hubbard Sign Problem with Complex-Valued Neural Networks
- A density of states approach to the hexagonal Hubbard model at finite density
- Free energy of the self-interacting relativistic lattice Bose gas at finite density
- Reduced critical slowing down for statistical physics simulations
- Efficient computations of continuous action densities of states for lattice models
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