Warm dark matter cosmologies have been widely studied as an alternative to the cold dark matter paradigm, the characteristic feature being a suppression of structure formation on small cosmological scales. A very similar situation occurs if standard cold dark matter particles are kept in local thermal equilibrium with a, possibly dark, relativistic species until the universe has cooled down to keV temperatures. We perform a systematic phenomenological study of this possibility, and classify all minimal models containing dark matter and an arbitrary radiation component that allow such a late kinetic decoupling. We recover explicit cases recently discussed in the literature and identify new classes of examples that are very interesting from a model-building point of view. In some of these models dark matter is inevitably self-interacting, which is remarkable in view of recent observational support for this possibility. Hence, dark matter models featuring late kinetic decoupling have the potential not only to alleviate the missing satellites problem but also to address other problems of the cosmological concordance model on small scales, in particular the cusp-core and too-big-too-fail problems, in some cases without invoking any additional input.
We develop a framework for joint constraints on the CO luminosity function based on power spectra (PS) and voxel intensity distributions (VID), and apply this to simulations of COMAP, a CO intensity mapping experiment. This Bayesian framework is based on a Markov chain Monte Carlo (MCMC) sampler coupled to a Gaussian likelihood with a joint PS + VID covariance matrix computed from a large number of fiducial simulations, and re-calibrated with a small number of simulations per MCMC step. The simulations are based on dark matter halos from fast peak patch simulations combined with the L CO (M halo ) model of Li et al. (2016). We find that the relative power to constrain the CO luminosity function depends on the luminosity range of interest. In particular, the VID is more sensitive at large luminosities, while the PS and the VID are both competitive at small and intermediate luminosities. The joint analysis is superior to using either observable separately. When averaging over CO luminosities ranging between L CO = 10 4 − 10 7 L , and over 10 cosmological realizations of COMAP Phase 2, the uncertainties (in dex) are larger by 58% and 30% for the PS and VID, respectively, when compared to the joint analysis (PS + VID). This method is generally applicable to any other random field, with a complicated likelihood, as long a fast simulation procedure is available.
Line-intensity mapping (LIM or IM) is an emerging field of observational work, with strong potential to fit into a larger effort to probe large-scale structure and small-scale astrophysical phenomena using multiple complementary tracers. Taking full advantage of such complementarity means, in part, undertaking line-intensity surveys with galaxy surveys in mind. We consider the potential for detection of a cross-correlation signal between COMAP and blind surveys based on photometric redshifts (as in COSMOS) or based on spectroscopic data (as with the HETDEX survey of Lyman-α emitters). We find that obtaining σ z /(1 + z) 0.003 accuracy in redshifts and 10 −4 sources per Mpc 3 with spectroscopic redshift determination should enable a CO-galaxy cross spectrum detection significance at least twice that of the CO auto spectrum. Either a future targeted spectroscopic survey or a blind survey like HETDEX may be able to meet both of these requirements.
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 μK2 greatly improves on the indirect upper limit of 420 μK2 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 μK2, and the cosmic molecular gas density, ρ H2 < 2.5 × 108 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.
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.
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