Inference of Boundaries in Causal Sets

TL;DR

This paper explores methods to identify and measure boundaries in causal sets within 1+1 dimensional Minkowski spacetime, enabling better understanding of boundary properties and their role in quantum gravity models.

Contribution

It introduces new algorithms to distinguish boundary types in causal sets and demonstrates their effectiveness through numerical experiments.

Findings

Timelike boundary volumes can be measured with 0.5% accuracy for flat boundaries.

Highly curved boundaries can be measured within 10% accuracy.

Algorithms successfully differentiate between null and non-null hypersurfaces.

Abstract

We investigate the extrinsic geometry of causal sets in $(1+1)$-dimensional Minkowski spacetime. The properties of boundaries in an embedding space can be used not only to measure observables, but also to supplement the discrete action in the partition function via discretized Gibbons-Hawking-York boundary terms. We define several ways to represent a causal set using overlapping subsets, which then allows us to distinguish between null and non-null bounding hypersurfaces in an embedding space. We discuss algorithms to differentiate between different types of regions, consider when these distinctions are possible, and then apply the algorithms to several spacetime regions. Numerical results indicate the volumes of timelike boundaries can be measured to within $0.5\%$ accuracy for flat boundaries and within $10\%$ accuracy for highly curved boundaries for medium-sized causal sets with $N=2^{14}$ spacetime elements.