s4cmb

Table of Contents

The package

Systematics For Cosmic Microwave Background (s4cmb) is a Python package developed to study the impact of instrumental systematic effects on measurements of CMB experiments based on bolometric detector technology. s4cmb provides a unified framework to simulate raw data streams in the time domain (TODs) acquired by CMB experiments scanning the sky, and to inject in these realistic instrumental systematics effect. The development of s4cmb is built on experience and needs of the analysis of data of the Polarbear ground-based experiment (see e.g. 1403.2369 and 1705.02907). It is designed to analyze real data, to guide the design of future instruments that require the estimation of specific systematic effects as well as to increase the realism of simulated data sets required in the development of data analysis methods. Users can currently model and study:

• Electrical crosstalk in the multiplexed readout.
• Relative gain-calibration uncertainty between the two detectors in a focal plane pixel.
• Time drift of the gains between two consecutive calibration measurements.
• Differential pointing between the two detectors in a pixel.
• ... more to come!

The simplicity of the s4cmb framework allows to easily add new instrumental systematics to be simulated according to the users' needs. As far as we know, s4cmb is the only dedicated package that enables the study of a wide range of instrumental simulations, from the instrument to the sky map, while being publicly available. For more general purposes, including some instrumental systematic effect simulations, users might also consider the use of TOAST, a software framework to simulate and process timestream data collected by telescopes focusing on efficient TOD manipulation on massively parallel architectures.

Requirements

The package is mainly written in python (>= 3.6), and it adopts several commonly used libraries in astronomy (astropy, healpy, ephem, pyslalib) and uses functions based on low-level languages wrapped in Python (e.g. Fortran with f2py) for speeding up the most critical part of the code without losing the flexibility provided by a simple python user-friendly interface. It has the following dependencies (see requirements.txt):

• numpy, matplotlib
• astropy, ephem, pyslalib, healpy (astro libs)
• f2py (interfacing with python)

The compilation of Fortran parts is done usually when you install the package (see setup.py), but we also provide a Makefile for more customized compilations (see the Makefile in s4cmb).

Installation

s4cmb is designed to be employed on systems of varying scale, from laptops to parallel supercomputing platforms thanks to its internal Message Passing Interface (MPI) support. We also support packaging the entire application into a Docker container for portability.

I just want to use the code:

You can easily install the package using pip

pip install s4cmb

In addition to use the code, I want to be a developer:

The best is to fork the repo from this github repository to your account and clone it to your machine. Once you have the repo cloned on your machine, use the makefile to compile the source

cd /path/to/s4cmb
pip install -r requirements.txt
make

Do not forget to update your PYTHONPATH. Just add in your bashrc:

s4cmbPATH=/path/to/the/s4cmb
export PYTHONPATH=$PYTHONPATH:$s4cmbPATH

Then run the test suite and the coverage:

./coverage_and_test.sh

It should print the actual coverage of the test suite, and exit with no errors.

Installation and usage at NERSC ===============

Again, you can easily install the package using pip

pip install s4cmb --user

Alternatively, if you want to do dev at NERSC and do a manual installation, it's better to keep most of your packages under Anaconda. I recommend to have a look first at the NERSC page describing how to use it.

The installation of s4cmb can be done in few steps:

• Clone the repo somewhere in your $HOME
• Install dependencies (see requirements.txt) using Anaconda
• Compile the source (using make in /path/s4cmb)

Working with Docker =============== Alternatively if you do not want install the package on your computer, we provide a docker image for s4cmb with always the latest version. Install docker on your computer, and pull the image:

docker pull julienpeloton/s4cmb:latest

Then create a new container and run an interactive session by just running

docker run -i -t julienpeloton/s4cmb:latest bash

Quick examples

We provide a quick end-to-end example for using the package:

python examples/test/simple_app.py -inifile examples/inifiles/simple_parameters.py -tag test

You can also run it on many processors, using MPI (you will need the package mpi4py):

mpirun -n <nproc> python examples/test/simple_app.py -inifile examples/inifiles/simple_parameters.py -tag test_MPI

where nproc should not be greater than the number of scans to run. Note that for NERSC users, we also provide a quick submission script for jobs on Cori (see examples/nersc_cori.batch).

s4cmb bootcamp

You can find a bootcamp in two parts (notebooks + examples) at s4cmb-resources. The goal of this bootcamp is to describe the basic parts of the API, and provide ready-to-use examples (for use on laptop and supercomputer).

TODO

• Add WHWP demodulation module.
• Add correlated noise simulator (and update mapmaking weights).

Main developers

• Julien Peloton (peloton at lal.in2p3.fr)
• Giulio Fabbian (g.fabbian at sussex.ac.uk)

Thanks to

• @ngoecknerwald: original author for a large part of the scanning strategy module.
• @giuspugl, @dpole, @joydidier, and all contributors for all valuable comments, tests, and feedbacks!

In the literature ===============

The package has already been used in a number of scientific and technical publications:

• Instrumental systematics biases in CMB lensing reconstruction: a simulation-based assessment (2011.13910)
• Development of Calibration Strategies for the Simons Observatory (1810.04633)
• Studies of Systematic Uncertainties for Simons Observatory: Detector Array Effects (1808.10491)
• Studies of Systematic Uncertainties for Simons Observatory: Polarization Modulator Related Effects (1808.07442)
• Iterative map-making with two-level preconditioning for polarized Cosmic Microwave Background data sets (1801.08937)

Support