COMAP Early Science. III. CO Data Processing

• Published in: The Astrophysical journal 2022-07, Vol.933 (2), p.184
• Main Authors: Foss, Marie K., Ihle, Håvard T., Borowska, Jowita, Cleary, Kieran A., Eriksen, Hans Kristian, Harper, Stuart E., Kim, Junhan, Lamb, James W., Lunde, Jonas G. S., Philip, Liju, Rasmussen, Maren, Stutzer, Nils-Ole, Uzgil, Bade D., Watts, Duncan J., Wehus, Ingunn K., Woody, David P., Bond, J. Richard, Breysse, Patrick C., Catha, Morgan, Church, Sarah E., Chung, Dongwoo T., Dickinson, Clive, Dunne, Delaney A., Gaier, Todd, Gundersen, Joshua Ott, Harris, Andrew I., Hobbs, Richard, Lawrence, Charles R., Murray, Norman, Readhead, Anthony C. S., Padmanabhan, Hamsa, Pearson, Timothy J., Rennie, Thomas J.
• Format: Article
• Language: English
• Subjects: Astronomical maps; Astrophysics; Calibration; Carbon monoxide; Cartography; Chi-square test; CO line emission; Cosmological evolution; Data processing; Filtration; Goodness of fit; High-redshift galaxies; Molecular gas; Noise; Pipelining (computers); Radio astronomy; Seasons; Statistical tests; Systematics; Transfer functions; White noise
• ISSN: 0004-637X; 1538-4357
• DOI: 10.3847/1538-4357/ac63ca

We describe the first-season CO Mapping Array Project (COMAP) analysis pipeline that converts raw detector readouts to calibrated sky maps. This pipeline implements four main steps: gain calibration, filtering, data selection, and mapmaking. 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 χ 2 and multiscale correlation tests. Applying this pipeline to the first-season COMAP data, we produce a data set 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.