Published September 22, 2016 | Version v1
Journal article

Simple indicators for Lorentzian causets

  • 1. CNR/ISTI, Pisa (Italy)
  • 2. Santa Cruz, CA (United States)

Description

Several classes of directed acyclic graphs have been investigated in the last two decades, in the context of the causal set program, in search for good discrete models of spacetime. We introduce some statistical indicators that can be used for comparing these graphs and for assessing their closeness to the ideal Lorentzian causal sets ('causets')—those obtained by sprinkling points in a Lorentzian manifold. In particular, with the reversed triangular inequality of Special Relativity in mind, we introduce 'longest/shortest path plots', an easily implemented tool to visually detect the extent to which a generic causet matches the wide range of path lengths between events of Lorentzian causets. This tool can attribute some degree of 'Lorentzianity'—in particular 'non-locality'—also to causets that are not (directly) embeddable and that, due to some regularity in their structure, would not pass the key test for Lorentz invariance: the absence of preferred reference frames. We compare the discussed indicators and use them for assessing causets both of stochastic and of deterministic, algorithmic origin, finding examples of the latter that behave optimally w.r.t. our longest/shortest path plots. (paper)

Availability note (English)

Available from http://dx.doi.org/10.1088/0264-9381/33/18/185004

Additional details

Publishing Information

Journal Title
Classical and Quantum Gravity
Journal Volume
33
Journal Issue
18
Journal Page Range
[41 p.]
ISSN
0264-9381
CODEN
CQGRDG

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
49032139
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; S72: PHYSICS OF ELEMENTARY PARTICLES AND FIELDS;
Descriptors DEI
DIAGRAMS; GRAPH THEORY; INDICATORS; LOCALITY; LORENTZ INVARIANCE; ORIGIN; RELATIVITY THEORY; SPACE-TIME; STOCHASTIC PROCESSES
Descriptors DEC
INFORMATION; INVARIANCE PRINCIPLES; MATHEMATICS