SO Likelihoods and Theories
A centralized package for likelihood and theory implementations for SO.
For a set of detailed instructions, please see here.
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.
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 unbinned SZ-cluster count likelihood based on the original ACT SZ clusters likelihood. -
LensingLikelihood
: Lensing power-spectrum likelihood, adapted from here. -
LensingLiteLikelihood
: A no-frills, simplelensing power spectrum. -
CrossCorrelationLikelihood
: A likelihood for cross-power spectra between galaxy surveys and CMB lensing maps. -
XcorrLikelihood
: An alternative likelihood for cross-power spectra between galaxy surveys and CMB lensing maps.
If you would like to contribute to SOLikeT, either addressing any of our Issues, adding features to the current likelihoods please read our Contributor Guidelines. If you plan on extending SOLikeT by adding new Likelihoods, please also have a look at the guidelines for doing this.
Please see these guidelines for instructions on bringing a new likelihood into soliket.
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.