Dynamical field inference and supersymmetry

TL;DR

This paper explores the theoretical connections between dynamical field inference, information field theory, and supersymmetry in stochastic systems, highlighting how supersymmetry breaking relates to chaos and impacts inference accuracy.

Contribution

It establishes a pedagogical link between DFI, IFT, and supersymmetric theory of stochastics, demonstrating the role of fermionic corrections in accurate posterior statistics.

Findings

Supersymmetry relates bosonic and fermionic fields in stochastic dynamics.

Breaking supersymmetry leads to chaotic behavior affecting inference.

Fermionic corrections are crucial for correct system trajectory statistics.

Abstract

Knowledge on evolving physical fields is of paramount importance in science, technology, and economics. Dynamical field inference (DFI) addresses the problem of reconstructing a stochastically driven, dynamically evolving field from finite data. It relies on information field theory (IFT), the information theory for fields. Here, the relations of DFI, IFT, and the recently developed supersymmetric theory of stochastics (STS) are established in a pedagogical discussion. In IFT, field expectation values can be calculated from the partition function of the full space-time inference problem. The partition function of the inference problem invokes a functional Dirac function to guarantee the dynamics, as well as a field-dependent functional determinant, to establish proper normalization, both impeding the necessary evaluation of the path integral over all field configurations. STS replaces these problematic expressions via the introduction of fermionic ghost and bosonic Lagrange fields, respectively. The action of these fields has a supersymmetry, which means there exists an exchange operation between bosons and fermions that leaves the system invariant. In contrast to this, measurements of the dynamical fields do not adhere to this supersymmetry. The supersymmetry can also be broken spontaneously, in which case the system evolves chaotically. This affects the predictability of the system and thereby make DFI more challenging. We investigate the interplay of measurement constraints with the non-linear chaotic dynamics of a simplified, illustrative system with the help of Feynman diagrams and show that the Fermionic corrections are essential to obtain the correct posterior statistics over system trajectories.