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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; [...]

COMAP Early Science. III. CO Data Processing

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  • Media type: E-Article
  • Title: COMAP Early Science. III. CO Data Processing
  • Contributor: 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; [...]
  • Published: American Astronomical Society, 2022
  • Published in: The Astrophysical Journal, 933 (2022) 2, Seite 184
  • Language: Not determined
  • DOI: 10.3847/1538-4357/ac63ca
  • ISSN: 0004-637X; 1538-4357
  • Origination:
  • Footnote:
  • Description: Abstract 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.
  • Access State: Open Access