Canonical tensor model through data analysis -- Dimensions, topologies, and geometries --
Taigen Kawano, Dennis Obster, Naoki Sasakura

TL;DR
This paper introduces a data analysis approach using tensor decomposition and persistent homology to extract topological and geometric information from the canonical tensor model, linking tensor dynamics to spacetime structures.
Contribution
It presents a novel method to interpret the CTM's tensors as spacetime topologies and geometries using established data analysis techniques, demonstrating its general applicability across dimensions.
Findings
Agreement with general relativity in fuzzy sphere models
Method applicable to any dimensions and topologies
Initial results support the CTM as a spacetime model
Abstract
The canonical tensor model (CTM) is a tensor model in Hamilton formalism and is studied as a model for gravity in both classical and quantum frameworks. Its dynamical variables are a canonical conjugate pair of real symmetric three-index tensors, and a question in this model was how to extract spacetime pictures from the tensors. We give such an extraction procedure by using two techniques widely known in data analysis. One is the tensor-rank (or CP, etc.) decomposition, which is a certain generalization of the singular value decomposition of a matrix and decomposes a tensor into a number of vectors. By regarding the vectors as points forming a space, topological properties can be extracted by using the other data analysis technique called persistent homology, and geometries by virtual diffusion processes over points. Thus, time evolutions of the tensors in the CTM can be interpreted as…
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