LINNA

Linna (Likelihood Inference Neural Network Accelerator) is a tool to accelerate Bayesian posterior inferences using artificial neural networks.

• Linna automatically builds training data, trains the neural network, and produces a Markov chain that samples the posterior.
• Linna reduces the runtime of survey cosmological analyses of the Dark Energy Survey by a factor of 8-50.
• Linna is verified to enable accurate and efficient sampling for Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) year ten multi-probe analyses.

• Linna is explicitly verified for the following three multi-probe analyses:

  • 3x2pt, a joint analysis of galaxy clustering, galaxy-galaxy lensing, and cosmic shear.
  • 4x2pt+N, a joint analysis of cluster--galaxy cross correlations, cluster lensing, cluster clustering, and cluster abundances.
  • 6x2pt+N, a joint analysis of data vectors in 3x2pt and 4x2pt+N.

Documentation

Read the docs at https://linna.readthedocs.io/en/latest/readme.html#documentation

Installation

git clone https://github.com/chto/linna.git
cd linna
python setup.py install

Attribution

Please cite the paper below if you find LINNA useful:

@article{linna2022,
author = {Chun-Hao To and Eduardo Rozo and Elisabeth Krause and Hao-Yi Wu and Risa H. Wechsler and Andrés N. Salcedo},
title = {LINNA: Likelihood Inference Neural Network Accelerator},
year = {2022},
journal={arXiv preprint arXiv:2203.05583}
}

Example

For example, if you want to sample a 33 dimensional gaussian spaces, you can do

import numpy as np
import matplotlib.pyplot as plt
from linna.main import ml_sampler
from linna.util import *
#Define gaussian
ndim = 33
init =  np.random.uniform(size=ndim)
means = np.random.uniform(size=ndim)
cov = np.diag(0.1*np.random.uniform(size=ndim))
priors = []
for i in range(ndim):
    priors.append({
        'param': 'test_{0}'.format(i),
        'dist': 'flat',
        'arg1': -5.,
        'arg2': 5.
    })
def theory(x, outdirs):
    x_new = deepcopy(x[1])
    return x_new
#LINNA
nwalkers = 4 #Number of mcmc walker
pool = None
outdir = os.path.abspath(os.getcwd())+"/out/2dgaussian/"
chain, logprob = ml_sampler(outdir, theory, priors, means, cov, init, pool, nwalkers, gpunode=None, nepoch=101)