TL;DR version:

The paper is focused on Cosmic Microwave Background data destriping, a map-making tecnique which exploits the fast scanning of instruments in order to efficiently remove correlated low frequency noise, generally caused by thermal fluctuations and gain instability of the amplifiers.

The paper treats in particular the case of destriping data from a polarimeter, i.e. an instrument which directly measures the polarized signal from the sky, which allows some simplification compared to the case of a simply polarization-sensitive radiometer.

I implemented a fully parallel python implementation of the algorithm based on:

• PyTrilinos for Distributed Linear Algebra via MPI
• HDF5 for I/O
• cython for improving the performance of the inner loops

The code is available on Github under GPL.

The output maps for about 30 days of the UCSB B-Machine polarimeter at 37.5 GHz are available on FigShare.

The experience of publishing with ASCOM was really positive, I received 2 very helpful reviews that drove me to work on several improvements on the paper.