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We present a new upper limit on the cosmic molecular gas density atz= 2.4–3.4 obtained using the first year of observations from the CO Mapping Array Project (COMAP). COMAP data cubes are stacked on the 3D positions of 243 quasars selected from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) catalog, yielding a 95% upper limit for flux from CO(1–0) line emission of 0.129 Jy km s⁻¹. Depending on the balance of the emission between the quasar host and its environment, this value can be interpreted as an average CO line luminosity$L_{\mathrm{CO}}^{\prime }$of eBOSS quasars of ≤1.26 × 10¹¹K km pc²s⁻¹, or an average molecular gas density${\rho }_{{\mathrm{H}}_2}$in regions of the Universe containing a quasar of ≤1.52 × 10⁸M_⊙cMpc⁻³. The$L_{\mathrm{CO}}^{\prime }$upper limit falls among CO line luminosities obtained from individually targeted quasars in the COMAP redshift range, and the${\rho }_{{\mathrm{H}}_2}$value is comparable to upper limits obtained from other line intensity mapping (LIM) surveys and their joint analyses. Further, we forecast the values obtainable with the COMAP/eBOSS stack after the full 5 yr COMAP Pathfinder survey. We predict that a detection is probable with this method, depending on the CO properties of the quasar sample. Based on the achieved sensitivity, we believe that this technique of stacking LIM data on the positions of traditional galaxy or quasar catalogs is extremely promising, both as a technique for investigating large galaxy catalogs efficiently at high redshift and as a technique for bolstering the sensitivity of LIM experiments, even with a fraction of their total expected survey data.

 

The CO Mapping Array Project (COMAP) is a carbon monoxide (CO) line intensity mapping experiment using a 19-feed 26–34 GHz focal plane spectrometer array on a 10.4 m dish at the Owens Valley Radio Observatory. We are developing a water vapor radiometer (WVR) that continuously measures the temporal variability of the atmosphere’s water vapor content along the telescope’s line of sight to better calibrate the COMAP science data. The WVR is designed to monitor the rotational transition line of water vapor around 22.2 GHz, with a spectral measurement between 18 and 26 GHz and a measurement of continuum at 28–30 GHz. Here we describe the COMAP WVR instrument system. 

Abstract We introduce a new Markov Chain Monte Carlo (MCMC) algorithm with parallel tempering for fitting theoretical models of horizon-scale images of black holes to the interferometric data from the Event Horizon Telescope (EHT). The algorithm implements forms of the noise distribution in the data that are accurate for all signal-to-noise ratios. In addition to being trivially parallelizable, the algorithm is optimized for high performance, achieving 1 million MCMC chain steps in under 20 s on a single processor. We use synthetic data for the 2017 EHT coverage of M87 that are generated based on analytic as well as General Relativistic Magnetohydrodynamic (GRMHD) model images to explore several potential sources of biases in fitting models to sparse interferometric data. We demonstrate that a very small number of data points that lie near salient features of the interferometric data exert disproportionate influence on the inferred model parameters. We also show that the preferred orientations of the EHT baselines introduce significant biases in the inference of the orientation of the model images. Finally, we discuss strategies that help identify the presence and severity of such biases in realistic applications. 

The thermal Sunyaev–Zel’dovich (tSZ) effect is a powerful tool with the potential for constraining directly the properties of the hot gas that dominates dark matter halos because it measures pressure and thus thermal energy density. Studying this hot component of the circumgalactic medium (CGM) is important because it is strongly impacted by star formation and active galactic nucleus (AGN) activity in galaxies, participating in the feedback loop that regulates star and black hole mass growth in galaxies. We study the tSZ effect across a wide halo-mass range using three cosmological hydrodynamical simulations: Illustris-TNG, EAGLE, and FIRE-2. Specifically, we present the scaling relation between the tSZ signal and halo mass and the (mass-weighted) radial profiles of gas density, temperature, and pressure for all three simulations. The analysis includes comparisons to Planck tSZ observations and to the thermal pressure profile inferred from the Atacama Cosmology Telescope (ACT) measurements. We compare these tSZ data to simulations to interpret the measurements in terms of feedback and accretion processes in the CGM. We also identify as-yet unobserved potential signatures of these processes that may be visible in future measurements, which will have the capability of measuring tSZ signals to even lower masses. We also perform internal comparisons between runs with different physical assumptions. We conclude (1) there is strong evidence for the impact of feedback atR₅₀₀, but that this impact decreases by 5R₅₀₀, and (2) the thermodynamic profiles of the CGM are highly dependent on the implemented model, such as cosmic-ray or AGN feedback prescriptions.

 

Background Health care and well-being are 2 main interconnected application areas of conversational agents (CAs). There is a significant increase in research, development, and commercial implementations in this area. In parallel to the increasing interest, new challenges in designing and evaluating CAs have emerged. Objective This study aims to identify key design, development, and evaluation challenges of CAs in health care and well-being research. The focus is on the very recent projects with their emerging challenges. Methods A review study was conducted with 17 invited studies, most of which were presented at the ACM (Association for Computing Machinery) CHI 2020 conference workshop on CAs for health and well-being. Eligibility criteria required the studies to involve a CA applied to a health or well-being project (ongoing or recently finished). The participating studies were asked to report on their projects’ design and evaluation challenges. We used thematic analysis to review the studies. Results The findings include a range of topics from primary care to caring for older adults to health coaching. We identified 4 major themes: (1) Domain Information and Integration, (2) User-System Interaction and Partnership, (3) Evaluation, and (4) Conversational Competence. Conclusions CAs proved their worth during the pandemic as health screening tools, and are expected to stay to further support various health care domains, especially personal health care. Growth in investment in CAs also shows the value as a personal assistant. Our study shows that while some challenges are shared with other CA application areas, safety and privacy remain the major challenges in the health care and well-being domains. An increased level of collaboration across different institutions and entities may be a promising direction to address some of the major challenges that otherwise would be too complex to be addressed by the projects with their limited scope and budget. 

Abstract We present the current state of models for the z ∼ 3 carbon monoxide (CO) line intensity signal targeted by the CO Mapping Array Project (COMAP) Pathfinder in the context of its early science results. Our fiducial model, relating dark matter halo properties to CO luminosities, informs parameter priors with empirical models of the galaxy–halo connection and previous CO (1–0) observations. The Pathfinder early science data spanning wavenumbers k = 0.051–0.62 Mpc −1 represent the first direct 3D constraint on the clustering component of the CO (1–0) power spectrum. Our 95% upper limit on the redshift-space clustering amplitude A clust ≲ 70 μ K 2 greatly improves on the indirect upper limit of 420 μ K 2 reported from the CO Power Spectrum Survey (COPSS) measurement at k ∼ 1 Mpc −1 . The COMAP limit excludes a subset of models from previous literature and constrains interpretation of the COPSS results, demonstrating the complementary nature of COMAP and interferometric CO surveys. Using line bias expectations from our priors, we also constrain the squared mean line intensity–bias product, Tb 2 ≲ 50 μ K 2 , and the cosmic molecular gas density, ρ H2 < 2.5 × 10 8 M ⊙ Mpc −3 (95% upper limits). Based on early instrument performance and our current CO signal estimates, we forecast that the 5 yr Pathfinder campaign will detect the CO power spectrum with overall signal-to-noise ratio of 9–17. Between then and now, we also expect to detect the CO–galaxy cross-spectrum using overlapping galaxy survey data, enabling enhanced inferences of cosmic star formation and galaxy evolution history. 

Abstract The CO Mapping Array Project (COMAP) aims to use line-intensity mapping of carbon monoxide (CO) to trace the distribution and global properties of galaxies over cosmic time, back to the Epoch of Reionization (EoR). To validate the technologies and techniques needed for this goal, a Pathfinder instrument has been constructed and fielded. Sensitive to CO(1–0) emission from z = 2.4–3.4 and a fainter contribution from CO(2–1) at z = 6–8, the Pathfinder is surveying 12 deg 2 in a 5 yr observing campaign to detect the CO signal from z ∼ 3. Using data from the first 13 months of observing, we estimate P CO ( k ) = −2.7 ± 1.7 × 10 4 μ K 2 Mpc 3 on scales k = 0.051 −0.62 Mpc −1 , the first direct three-dimensional constraint on the clustering component of the CO(1–0) power spectrum. Based on these observations alone, we obtain a constraint on the amplitude of the clustering component (the squared mean CO line temperature bias product) of Tb 2 < 49 μ K 2 , nearly an order-of-magnitude improvement on the previous best measurement. These constraints allow us to rule out two models from the literature. We forecast a detection of the power spectrum after 5 yr with signal-to-noise ratio (S/N) 9–17. Cross-correlation with an overlapping galaxy survey will yield a detection of the CO–galaxy power spectrum with S/N of 19. We are also conducting a 30 GHz survey of the Galactic plane and present a preliminary map. Looking to the future of COMAP, we examine the prospects for future phases of the experiment to detect and characterize the CO signal from the EoR. 

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, includingχ²and 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.

 

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 scalesk= 0.051–0.62 Mpc⁻¹, we estimateP_CO(k) = −2. 7 ± 1.7 × 10⁴μK²Mpc³, the first direct 3D constraint on the clustering component of the CO(1–0) power spectrum in the literature.

 

Line intensity mapping (LIM) is a new technique for tracing the global properties of galaxies over cosmic time. Detection of the very faint signals from redshifted carbon monoxide (CO), a tracer of star formation, pushes the limits of what is feasible with a total-power instrument. The CO Mapping Project Pathfinder is a first-generation instrument aiming to prove the concept and develop the technology for future experiments, as well as delivering early science products. With 19 receiver channels in a hexagonal focal plane arrangement on a 10.4 m antenna and an instantaneous 26–34 GHz frequency range with 2 MHz resolution, it is ideally suited to measuring CO (J= 1–0) fromz∼ 3. In this paper we discuss strategies for designing and building the Pathfinder and the challenges that were encountered. The design of the instrument prioritized LIM requirements over those of ancillary science. After a couple of years of operation, the instrument is well understood, and the first year of data is already yielding useful science results. Experience with this Pathfinder will guide the design of the next generations of experiments.