COMAP Early Science: III. CO Data Processing

TL;DR

This paper details the development of a comprehensive data processing pipeline for COMAP's first season, transforming raw detector data into calibrated sky maps with low noise and bias, and discusses initial power spectrum results.

Contribution

It introduces a new, detailed pipeline for processing COMAP data, including calibration, filtering, and map-making, and evaluates its performance on first-season data.

Findings

Produced low-noise, calibrated sky maps from first-season data

Identified residual systematics in one scanning strategy leading to its exclusion

Presented initial power spectrum results and analyzed data processing efficiency

Abstract

We describe the first season COMAP analysis pipeline that converts raw detector readouts to calibrated sky maps. This pipeline implements four main steps: gain calibration, filtering, data selection, and map-making. Absolute gain calibration relies on a combination of instrumental and astrophysical sources, while relative gain calibration exploits real-time total-power variations. High efficiency filtering is achieved through spectroscopic common-mode rejection within and across receivers, resulting in nearly uncorrelated white noise within single-frequency channels. Consequently, near-optimal but biased maps are produced by binning the filtered time stream into pixelized maps; the corresponding signal bias transfer function is estimated through simulations. Data selection is performed automatically through a series of goodness-of-fit statistics, including $\chi^2$ and multi-scale correlation tests. Applying this pipeline to the first-season COMAP data, we produce a dataset with very low levels of correlated noise. We find that one of our two scanning strategies (the Lissajous type) is sensitive to residual instrumental systematics. As a result, we no longer use this type of scan and exclude data taken this way from our Season 1 power spectrum estimates. We perform a careful analysis of our data processing and observing efficiencies and take account of planned improvements to estimate our future performance. Power spectrum results derived from the first-season COMAP maps are presented and discussed in companion papers.