monte_carlo_spinfoams

Codes, notebooks and data for the computation of the self-energy and vertex renormalization spinfoam amplitudes. The algorithm is based on Monte Carlo simulations.

https://github.com/pietropaolofrisoni/monte_carlo_spinfoams

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loop-quantum-gravity monte-carlo spinfoams supercomputing

Last synced: 6 months ago · JSON representation ·

Repository

Codes, notebooks and data for the computation of the self-energy and vertex renormalization spinfoam amplitudes. The algorithm is based on Monte Carlo simulations.

Basic Info

Host: GitHub
Owner: PietropaoloFrisoni
License: gpl-3.0
Language: Jupyter Notebook
Default Branch: master
Homepage:
Size: 24.9 MB

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loop-quantum-gravity monte-carlo spinfoams supercomputing

Created over 3 years ago · Last pushed almost 3 years ago

Metadata Files

Readme License Citation

Monte Carlo divergences

The Julia codes are parallelized on the available cores. It is therefore advisable for the performance to parallelize the codes keeping into account the number of CPU cores present on the system.

A full list of the employed Julia packages can be found in ./inc/pkgs.jl. Before executing the source codes, all packages must be installed.

The Julia's Just-in-Time compiler is such that the first execution of functions is considerably slower that following ones, and it also allocates much more memory. To avoid this, you can use the DaemonMode package.

Usage

To execute the Julia codes (on a single machine with the synthax below) you can run the following command:

$JULIA_EXECUTABLE_PATH -p [N-1] $JULIA_CODE_PATH $ARGS

where [N-1] is the number of workers. The ARGS parameter depends on the specific kind of computation. We show an example below.

Example: Self energy EPRL with monte carlo sampling

ARGS = DATA_SL2CFOAM_FOLDER CUTOFF JB DL_MIN DL_MAX IMMIRZI STORE_FOLDER MONTE_CARLO_ITERATIONS NUMBER_OF_TRIALS

where:

DATA_SL2CFOAM_FOLDER: folder with fastwigxj tables where boosters (and possibly vertices) are retrieved/stored
CUTOFF: the maximum value of bulk spins
JB: value of boundary spins
DL_MIN: minimum value of truncation parameter over auxiliary spins
DL_MAX: maximum value of truncation parameter over auxiliary spins
IMMIRZI: value of Immirzi parameter
STORE_FOLDER: folder where data are saved
MONTE_CARLO_ITERATIONS: number of monte carlo sampling for each trial
NUMBER_OF_TRIALS: number of trials

Additionally, you can specify the weights $\mu1, \mu2 \dots \mun$ on bulk faces inside the code script, with the vector `FACEWEIGHTS_VEC`. Each bulk face with spin $j$ has dimension $(2j+1)^{\mu}$, and the code computes all amplitudes with provided weights.

Owner

Name: Pietropaolo Frisoni
Login: PietropaoloFrisoni
Kind: user
Location: London, Ontario, Canada
Company: ComputeCanada

Repositories: 2
Profile: https://github.com/PietropaoloFrisoni

Ph.D. candidate in physics at Western University in London, Ontario, Canada. I use HPC techniques to study quantum gravity

Citation (CITATION.cff)

cff-version: 1.2.0
message: 'If you use this software, please cite it as below.'
authors:
    - family-names: Frisoni
      given-names: Pietropaolo 
      orcid: https://orcid.org/0000-0002-0099-0409
    - family-names: Pietro
      given-names: Donà 
      orcid: https://orcid.org/0000-0001-7341-0682      
title: 'Summing bulk quantum numbers with Monte Carlo in spin foam theories'
version: 1.0.1
doi: https://doi.org/10.1103/PhysRevD.107.106008
date-released: 2023-01-25
url: https://github.com/PietropaoloFrisoni/Monte_Carlo_spinfoams

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