Random perfect lattices and the sphere packing problem
Alexei Andreanov, Antonello Scardicchio

TL;DR
This paper investigates the properties of randomly generated perfect lattices in moderate dimensions to understand their sphere packing densities, revealing they are denser than known families and exhibit complex network structures.
Contribution
It introduces a randomized algorithm to study perfect lattices, providing new insights into their density distribution, network structure, and potential bounds on packing fractions in higher dimensions.
Findings
Perfect lattices are denser than classical lattice families.
Random ensembles recover known packers at low temperatures.
The network of perfect lattices is scale-free with exponent ~2.6.
Abstract
Motivated by the search for best lattice sphere packings in Euclidean spaces of large dimensions we study randomly generated perfect lattices in moderately large dimensions (up to d=19 included). Perfect lattices are relevant in the solution of the problem of lattice sphere packing, because the best lattice packing is a perfect lattice and because they can be generated easily by an algorithm. Their number however grows super-exponentially with the dimension so to get an idea of their properties we propose to study a randomized version of the algorithm and to define a random ensemble with an effective temperature in a way reminiscent of a Monte-Carlo simulation. We therefore study the distribution of packing fractions and kissing numbers of these ensembles and show how as the temperature is decreased the best know packers are easily recovered. We find that, even at infinite temperature,…
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Taxonomy
TopicsGraph Theory and Algorithms · Theoretical and Computational Physics
