期刊
ASTROPHYSICAL JOURNAL
卷 933, 期 2, 页码 -出版社
IOP Publishing Ltd
DOI: 10.3847/1538-4357/ac63ca
关键词
-
资金
- National Science Foundation [1517108, 1517288, 1517598, 1518282, 1910999]
- Keck Institute for Space Studies under The First Billion Years: A Technical Development Program for Spectral Line Observations''
- internal Research and Technology Development program
- CITA/Dunlap Institute postdoctoral fellowship
- David Dunlap family
- University of Toronto
- STFC Consolidated Grant [ST/ P000649/1]
- Research Council of Norway [251328, 274990]
- European Research Council (ERC) under the Horizon 2020 Research and Innovation Program [819478]
- University of Miami
- Robert A. Millikan Fellowship from Caltech
- Swiss National Science Foundation [PZ00P2_179934]
- James Arthur Postdoctoral Fellowship
- Swiss National Science Foundation (SNF) [PZ00P2_179934] Funding Source: Swiss National Science Foundation (SNF)
- Division Of Astronomical Sciences
- Direct For Mathematical & Physical Scien [1517598, 1517108, 1517288] Funding Source: National Science Foundation
- Division Of Astronomical Sciences
- Direct For Mathematical & Physical Scien [1910999, 1518282] Funding Source: National Science Foundation
We describe the analysis pipeline of the first-season CO Mapping Array Project (COMAP) that converts raw detector readouts to calibrated sky maps. This pipeline includes four main steps: gain calibration, filtering, data selection, and mapmaking. The resulting data set and maps are analyzed and evaluated in terms of data processing and observing efficiencies.
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 chi (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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据