SOLikeT

SO Likelihoods and Theories

A centralized package for likelihood and theory implementations for SO.

Installation

git clone https://github.com/simonsobs/soliket
cd soliket
pip install -e .

You will also need to either run

pip install camb

or, for a fuller cobaya install:

cobaya-install cosmo -p /your/path/to/cobaya/packages

To run tests, you will also need the original LAT_MFlike package:

pip install git+https://github.com/simonsobs/lat_mflike

Then, you can run tests with

pip install pytest
pytest -v .

Please raise an issue if you have trouble installing or any of the tests fail.

Contains

This repo currently implements the following specific likelihoods:

• MFLike: the SO LAT multi-frequency TT-TE-EE power spectrum likelihood. (Adapted from, and tested against, the original implementation here).
• ClusterLikelihood: An SZ-cluster count likelihood based on the original ACT SZ clusters likelihood.
• LensingLikelihood: Lensing power-spectrum likelihood, adapted from here.
• LensingLiteLikelihood: A no-frills, simple $\chi^2$ lensing power spectrum.

Extending

Please see these guidelines for instructions on bringing a new likelihood into soliket.

Usage

These likelihoods are designed for direct use with cobaya. This means that they may be specified directly when creating a cobaya Model. E.g., if you wanted to compute the likelihood of the simulated lensing data, you could do the following:

from cobaya.yaml import yaml_load
from cobaya.model import get_model

info_yaml = """
debug: True

likelihood:
  soliket.LensingLiteLikelihood:
    sim_number: 1
    stop_at_error: True

params:
  # Sampled
  logA:
    prior:
      min: 2.6
      max: 3.5
    proposal: 0.0036
    drop: True
    latex: \log(10^{10} A_\mathrm{s})
  As:
    value: "lambda logA: 1e-10*np.exp(logA)"
    latex: A_\mathrm{s}
  ns:
    prior:
      min: 0.9
      max: 1.1
    proposal: 0.0033
    latex: n_\mathrm{s}


theory:
  camb:
    stop_at_error: False
    extra_args:
      lens_potential_accuracy: 1

"""

info = yaml_load(info_yaml)
model = get_model(info)

The likelihood could then be either directly computed as

model.loglike(dict(logA=3.0, ns=0.98))

and used outside of cobaya (e.g., directly passed to emcee or some other sampler or optimizer), or this same YAML setup (with an additional 'sampler' block specified) could be used as input to cobaya-run to have cobaya manage the sampling.

For more information on how to use cobaya, check out its documentation.