SimpleMC

A code for cosmological parameter estimation where only expansion history matters. Simplemc contains several tools to perform parameter estimation and maximum likelihood estimation.

This code is not intendent as a replacement of CosmoMC. It simply reimplements the homogeneus part of cosmomc code. This allows one to fit BAO data, SNeIa, cosmic chronometers and fsigma8 among others.

Any crazy model can be implemented without much hassle and very quickly. One just needs to define how Hubble parameter varies with redshift. It might also be useful for pedagogical reasons.

Temporary documentation in: https://igomezv.github.io/SimpleMC/

You can install simplemc on your computer using:

 $ git clone https://github.com/ja-vazquez/SimpleMC 

 $ cd SimpleMC

 $ pip3 install -e .

then you can delete the cloned repo because you must have simplemc installed locally.

Other way to install simplemc (without clonning) is:

 $ pip3 install -e git+https://github.com/ja-vazquez/SimpleMC#egg=simplemc

In the requirements.txt file are the basic libraries to run simplemc, but some functions such as graphics or neural networks may not be available. To get the full requirements use requirements_full.txt. You can manually install these dependencies with pip3 install -r requirements_full.txt.

Quick start:

A quick start with the code, try:

python3 test.py

You can set the baseConfig.ini with the analyzer, model and datasets that you need.

Directory structure:

simplemc (root) - actual executables - you should read them like scripts

analyzers - analysis code (samplers and optimizers)

data - data files

cosmo - Cosmoligical issues

likelihoods - Several likelihoods for several datasets

models - Cosmological models based on LCDM

tools - various tools we use for plotting, etc.

How does it work:

It was supposed to be minimal, allowing any one to add models, without being spaghetti.

There are three basic kind of objects:

• Cosmology objects that define models. These are based on BaseCosmology.py

• Likelihood objects that calculate Likelihood given some theory predictions from Cosmoloy. These are based on BaseLikelihood. You tell them the theory you want to use with setTheory and then you can ask them about loglikelihood(). They can also tell you which Parameters they have with freeParameters. These are based on Parameter.py and are really simple -- name defines the parameter.

• Analyzers -- these take likelihoods and do something with it (see below).

To see how data is used, review Likelihood folder.

To see how models are created, have a look, for example at LCDMCosmology.py and oLCDMCosmology.py.

Parameters and priors are defined in paramDefs.py

LCDMCosmology inherits BaseCosmology and defines three functions:

• freeParameters - here we announced what are the parameters in this model

• updateParameters - here we process a request to update parameters. Here we store parameters, but must also update rs of the BaseCosmogy if it is affected. At the moment we keep rs constant.

• HSSquared_a - this is the relative Hubble parameter squared, in simple ternms H(a)2/H02

BaseCosmology deals with integrating the above to various distance moduli. It also handles the prefactors of the form c/(H0rs). We can either have h=H0/100 as a parameter and then believe that rs is whatever it is, or have the entire phenomenological prefactor c/(H0rs) as a free parameter. One can switch this on as demonstrated in Run/TestRun.py with T.setVaryPrefactor().

RadiationAndNeutrinos.py deals with, well, radiation and neutrino components. NuDensity.py calculates the annoying neutrino integral.

Analyzers:

So, one can then create a composite Likelihood by saying e.g.

L=CompositeLikelihood([ DR11LOWZ(), DR11CMASS(), DR11LyaAuto(), DR11LyaCross() ])

and a then connect it to a theory, like L.setTheory(oLCDMCosmology())

This is now a nice package: L.freeParameters() will tell you which parameters you can jiggle. L.updateParams() allows you to update them. L.loglike() returns the total log likelihood.

Different Analyzers can use this interface to do different things with this likelihood, without knowing anything about the underlying theory. There are two attached:

• MaxLikeAnalyzer - finds the maximum and then uses second derivative to get errors, only that that doesn't quite work and I think this is due to linear interpolation in the chi2 tables

• MCMCAnalyzer - a vanilla MCMC samples compatible with CosmoMC format

• Nested Sampling - through a modified version of the dynesty library. It calculates Bayesian evidence and allows model comparison.

• Genetic algorithms - finds the maximum with the genetic algorithms of the deap library.

• emcee - emcee library.

• mcevidence - approximate Bayesian evidence to emcee and MCMC.