COMAP Early Science. V. Constraints and Forecasts at z ∼ 3

Chung, Dongwoo T.; Breysse, Patrick C.; Cleary, Kieran; Ihle, Håvard T.; Padmanabhan, Hamsa; Silva, Marta B.; Bond, J. Richard; Borowska, Jowita; Catha, Morgan; Church, S.; Dunne, Delaney A.; Eriksen, H. K.; Foss, Marie Kristine; Gaier, T.; Gundersen, Joshua O.; Harper, Stuart E.; Harris, A. I.; Hensley, Brandon; Hobbs, Richard; Keating, Laura C.; Kim, Junhan; Lamb, James W.; Lawrence, C. R.; Lunde, Jonas G. S.; Murray, Norman; Pearson, T. J.; Philip, Liju; Rasmussen, Maren; Readhead, A. C. S.; Rennie, Thomas J.; Stutzer, Nils-Ole; Uzgil, Bade; Viero, Marco P.; Watts, Duncan J.; Wechsler, Risa H.; Wehus, I. K.; Woody, D. P.

doi:10.3847/1538-4357/ac63c7

Cited by 31 publications

(38 citation statements)

References 83 publications

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“…This demonstrates that we are in fact averaging down the noise, as expected for uncorrelated noise, and that the various potential systematic errors are suppressed to a level below the noise. At this point in the COMAP survey, this is a key outcome, given that our fiducial theoretical model predicts a signal on the order of kP CO ∼ 10 3 μK 2 Mpc 2 at our target redshift (Chung et al 2022), well below the noise level shown here. This signal estimate is highly uncertain, however, and as discussed in Chung et al (2022) these data already rule out some of the most optimistic models.…”

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confidence: 50%

“…At this point in the COMAP survey, this is a key outcome, given that our fiducial theoretical model predicts a signal on the order of kP CO ∼ 10 3 μK 2 Mpc 2 at our target redshift (Chung et al 2022), well below the noise level shown here. This signal estimate is highly uncertain, however, and as discussed in Chung et al (2022) these data already rule out some of the most optimistic models.…”

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confidence: 50%

“…We estimate the frequency binning transfer function, T freq (k), by comparing power spectra of the simulated signal (using the default model in Chung et al 2022) on a high-resolution frequency grid to power spectra of the simulated signal on our current frequency grid, both binned in our current k ∥ × k ⊥ bins. The transfer function derived using 50 such signal simulations is shown in Figure 3.…”

Section: Frequency Resolution Transfer Functionmentioning

confidence: 99%

“…Since the first-season COMAP instrument observes at frequencies between 26 and 34 GHz, and the rotational CO(1-0) transition has a rest frequency of 115 GHz, the current measurements trace galaxy formation at redshifts between z = 2.4 and 3.4, during "the epoch of galaxy assembly." The current limits, forecasts, and modeling at these redshifts is discussed in Chung et al (2022), while a future phase of COMAP, targeting "the epoch of reionization," is discussed in Breysse et al (2022). The use of this instrument for a galactic survey is presented in Rennie et al (2022).…”

Section: Introductionmentioning

confidence: 99%

“…However, while compressing hundreds of terabytes of raw data into a handful of power spectrum coefficients certainly makes the interpretation of the data easier in terms of theoretical comparisons, it also makes the final estimates highly sensitive to small systematic effects and instrumental noise. To guide our intuition, we note that current theories predict an intrinsic CO standard deviation per resolution element of no more than a few microkelvin (Breysse et al 2014;Li et al 2016;Chung et al 2022), which is to be compared with a typical system temperature of 44 K for the COMAP instrument; or atmospheric fluctuations of a few kelvin; or sidelobe contributions of a few millikelvin. All such effects must therefore be suppressed by many orders of magnitude in order to establish robust astrophysical constraints.…”

Section: Introductionmentioning

confidence: 99%

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COMAP Early Science. IV. Power Spectrum Methodology and Results

Ihle

Borowska

Cleary

et al. 2022

ApJ

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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 estimated in this way have allowed us to identify a systematic error associated with one of our two scanning strategies, believed to be due to residual ground or atmospheric contamination. We omit these data from our analysis and no longer use this scanning technique for observations. We present the power spectra from our first season of observing, and demonstrate that the uncertainties are integrating as expected for uncorrelated noise, with any residual systematics suppressed to a level below the noise. Using the FPXS method, and combining data on scales k = 0.051–0.62 Mpc−1, we estimate P CO(k) = −2. 7 ± 1.7 × 104 μK2 Mpc3, the first direct 3D constraint on the clustering component of the CO(1–0) power spectrum in the literature.

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confidence: 50%

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confidence: 50%

Section: Frequency Resolution Transfer Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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COMAP Early Science. IV. Power Spectrum Methodology and Results

Ihle

Borowska

Cleary

et al. 2022

ApJ

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[CII] luminosity models and large-scale image cubes based on COSMOS 2020 and ALPINE-ALMA [CII] data back to the epoch of reionisation

Clarke,

Karoumpis,

Riechers

et al. 2024

A&A

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We have implemented a novel method to create simulated CII emission line intensity mapping (LIM) data cubes using COSMOS 2020 galaxy catalogue data. It allows us to provide solid lower limits for previous simulation-based model predictions and the expected signal strength of upcoming surveys. We applied CII 158mu m luminosity models to COSMOS 2020 to create LIM cubes covering a $1.2 sky area. These models were derived using galaxy bulk property data from the ALPINE-ALMA survey over the redshift range of $4.4<z<5.9$, while additional models were taken from the literature. The LIM cubes cover $3.42<z<3.87$, $4.14<z<4.76$, $5.34<z<6.31$, and $6.75<z<8.27$, matched to planned observations from the EoR-Spec module of the Prime-Cam instrument in the Fred Young Submillimeter Telescope (FYST). We also created predictions including additional galaxies below current detection limits by `extrapolating' from the faint end of the COSMOS 2020 luminosity function, comparing these to predictions from the literature. In addition, we computed the signal-to-noise (S/N) ratios for the power spectra, using parameters from the planned FYST survey with predicted instrumental noise levels. We find lower limits for the expected power spectrum using the likely incomplete empirical data: when normalised by $2 the amplitudes at $k=1\ Mpc $ are $3.06 Jy\,sr )^2$ for the aforementioned redshift ranges. For the extrapolated sample, the power spectra are consistent with prior predictions, indicating that extrapolation is a viable method for creating mock LIM cubes. In this case, we expect a result of S/N$>$1 when using FYST parameters. However, our high-redshift results remain inconclusive because of the poor completeness of COSMOS 2020 at $z>6.3$. These predictions will be improved on the basis of of future JWST data.

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[CII] line intensity mapping the epoch of reionization with the Prime-Cam on FYST

Karoumpis,

Magnelli,

Romano-Díaz

et al. 2024

A&A

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The Fred Young Submillimeter Telescope (FYST) line intensity mapping (LIM) survey will measure the power spectrum (PS) of the singly ionized carbon 158 $ fine-structure line CII to trace the appearance of the first galaxies that emerged during and right after the epoch of reionization (EoR, $6<z<9$). We aim to quantify the contamination of the (post-)EoR CII LIM signal by foreground carbon monoxide (CO) line emission ($3 < J_ up < 12$) and assess the efficiency to retrieve this CII LIM signal by the targeted masking of bright CO emitters. Using the IllustrisTNG300 simulation, we produced mock CO intensity tomographies based on empirical star formation rate-to-CO luminosity relations. Combining these predictions with the CII PS predictions of the first paper of this series, we evaluated a masking technique where the interlopers are identified and masked using an external catalog whose properties are equivalent to those of a deep Euclid survey. Prior to masking, our CII PS forecast is an order of magnitude lower than the predicted CO contamination in the 225 GHz ( CII emitted at $z=6.8-8.3$) band of the FYST LIM survey, at the same level in its 280 GHz ( CII emitted at $z=5.3-6.3$) and 350 GHz ( CII emitted at $z=4.1-4.8$) bands, and an order of magnitude higher in its 410 GHz ( CII emitted at $z=3.4-3.9$) band. For our fiducial model, the optimal masking depth is reached when less than 10<!PCT!> of the survey volume is masked at 350 and 410 GHz but around 40<!PCT!> at 280 GHz and 60 <!PCT!> at 225 GHz. At these masking depths we anticipate a detection of the CII PS at 350 and 410 GHz, a tentative detection at 280 GHz, whereas at 225 GHz the CO signal still dominates our model. In the last case, alternative decontamination techniques will be needed.

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COMAP Early Science. V. Constraints and Forecasts at z ∼ 3

Cited by 31 publications

References 83 publications

COMAP Early Science. IV. Power Spectrum Methodology and Results

COMAP Early Science. IV. Power Spectrum Methodology and Results

[CII] luminosity models and large-scale image cubes based on COSMOS 2020 and ALPINE-ALMA [CII] data back to the epoch of reionisation

[CII] line intensity mapping the epoch of reionization with the Prime-Cam on FYST

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