2022
DOI: 10.3847/1538-4357/ac63c5
|View full text |Cite
|
Sign up to set email alerts
|

COMAP Early Science. IV. Power Spectrum Methodology and Results

Abstract: We present the power spectrum methodology used for the first-season COMAP analysis, and assess the quality of the current data set. The main results are derived through the Feed–Feed Pseudo-Cross-Spectrum (FPXS) method, which is a robust estimator with respect to both noise modeling errors and experimental systematics. We use effective transfer functions to take into account the effects of instrumental beam smoothing and various filter operations applied during the low-level data processing. The power spectra … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
24
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
7

Relationship

2
5

Authors

Journals

citations
Cited by 23 publications
(25 citation statements)
references
References 37 publications
1
24
0
Order By: Relevance
“…For example, this is why the Lidz11 model produces such a low S/N for the CO(2-1) auto spectrum, as the factor of 8 difference between CO(1-0) and (2-1) means that the low-redshift interloper is much brighter than the EoR line. This is not precisely correct, as we can perform internal cross correlations within our raw data to remove any overall bias from instrument noise (Ihle et al 2022), while we cannot do the same with a signal on the sky. However, as mentioned above in a real analysis we could likely do more to mask out the lower-redshift line, which we have not accounted for here.…”
Section: Sensitivity Forecastsmentioning
confidence: 99%
See 1 more Smart Citation
“…For example, this is why the Lidz11 model produces such a low S/N for the CO(2-1) auto spectrum, as the factor of 8 difference between CO(1-0) and (2-1) means that the low-redshift interloper is much brighter than the EoR line. This is not precisely correct, as we can perform internal cross correlations within our raw data to remove any overall bias from instrument noise (Ihle et al 2022), while we cannot do the same with a signal on the sky. However, as mentioned above in a real analysis we could likely do more to mask out the lower-redshift line, which we have not accounted for here.…”
Section: Sensitivity Forecastsmentioning
confidence: 99%
“…Throughout this work, we assume a flat Λ cold dark matter cosmology (CDM) consistent with the Planck 2018 results (Planck Collaboration et al 2020). More detail on the COMAP Pathfinder can be found in the other papers in this series, including discussions of the instrumental hardware (Lamb et al 2022), the data reduction pipeline (Foss et al 2022), the power spectrum analysis (Ihle et al 2022), the science and modeling implications (Chung et al 2022), and the auxiliary Galactic plane observations (Rennie et al 2022).…”
Section: Introductionmentioning
confidence: 99%
“…As Foss et al (2022) note in their Section 4.2, future observing seasons should improve the rate at which we acquire science-quality integration time through a combination of improvements in hardware, observing efficiency, and analysis. This implies that by the end of Year 5 of the Pathfinder campaign (Y5), sensitivity relative to the current Y1 power spectrum results of Ihle et al (2022) will improve not by a factor of 5 but by as much as a factor of 69 over the Field 1 Y1 result (which, as noted above, accounts for much of the current sensitivity). Of interest is how this final Pathfinder sensitivity will enable exclusion or detection not only of our fiducial UM +COLDz+COPSS model but also of other models previously considered in the literature.…”
Section: Expectations For Comap Pathfinder Future Science Resultsmentioning
confidence: 91%
“…The present state of COMAP observations does not yet allow for the kinds of analyses that we forecast in Section 4. However, the P(k) result 20 obtained by Ihle et al (2022) 20 Strictly speaking, as Ihle et al (2022) note in their Section 3.1, the result is based on a pseudo-power-spectrum measurement and may have some residual mode-mixing bias. However, their Figure 1 also shows that this mode-mixing bias likely is a small effect (5%-30%) that enhances the pseudo-spectrum relative to the true signal.…”
Section: Implications Of Comap Early Science Power Spectrum Measurementsmentioning
confidence: 99%
See 1 more Smart Citation