How to Cite This Software

If CDT++ contributes to research or a project, cite it using the structured metadata in CITATION.cff. GitHub and other citation tools can generate BibTeX, APA, and additional formats from that file.

Quick citation for the current declared release:

Adam Getchell. 2026. CDT-plusplus: Causal Dynamical Triangulations in C++.

Version 1.0.0-rc1. GitHub. https://github.com/acgetchell/CDT-plusplus

The sections below are grouped by scientific domain. Cite the entries relevant to the algorithms or results used in a given work. Source documentation links directly to the applicable entry so that provenance stays close to the implementation while complete metadata remains centralized here.

Foundational Causal Dynamical Triangulations Theory

Original CDT framework

CDT framework (2001)

J. Ambjørn, J. Jurkiewicz, and R. Loll, “Dynamically triangulating Lorentzian quantum gravity,” Nuclear Physics B 610, no. 1–2 (2001), 347–382. DOI: 10.1016/S0550-3213(01)00297-8

Three-dimensional CDT (2001)

J. Ambjørn, J. Jurkiewicz, and R. Loll, “Nonperturbative 3D Lorentzian quantum gravity,” Physical Review D 64, no. 4 (2001), 044011. DOI: 10.1103/PhysRevD.64.044011

Monte Carlo Methods

Metropolis-Hastings algorithm

W. K. Hastings, “Monte Carlo sampling methods using Markov chains and their applications,” Biometrika 57, no. 1 (1970), 97–109. DOI: 10.1093/biomet/57.1.97

Regge Calculus and Discrete Action

Regge calculus

T. Regge, “General relativity without coordinates,” Il Nuovo Cimento 19, no. 3 (1961), 558–571. DOI: 10.1007/BF02733251

Simplicial Topology and Local Moves

Pachner moves

U. Pachner, “P.L. homeomorphic manifolds are equivalent by elementary shellings,” European Journal of Combinatorics 12, no. 2 (1991), 129–145. DOI: 10.1016/S0195-6698(13)80080-7

Computational Geometry and Random-Number Generation

CGAL triangulations

The CGAL Project, CGAL User and Reference Manual. CGAL Editorial Board, 6.2 edition (2026). https://doc.cgal.org/6.2/Manual/packages.html

PCG random-number generators

M. E. O’Neill, “PCG: A family of simple fast space-efficient statistically good algorithms for random number generation,” Harvey Mudd College Computer Science Department technical report HMC-CS-2014-0905 (2014). https://www.pcg-random.org/paper.html