Search for: All records

The sum of neutrino masses can be measured cosmologically, as the sub-eV particles behave as “hot” dark matter whose main effect is to suppress the clustering of matter compared to a universe with the same amount of purely cold dark matter. Current astronomical data provide an upper limit on $\sum m_{\nu }$between 0.07–0.12 eV at 95% confidence, depending on the choice of data. This bound assumes that the cosmological model is $\mathrm{\Lambda }$Cold Dark Matter ( $\mathrm{\Lambda }\mathrm{CDM}$), where dark energy is a cosmological constant, the spatial geometry is flat, and the primordial fluctuations follow a pure power law. Here, we update studies on how the mass limit degrades if we relax these assumptions. To existing data from the satellite we add new gravitational lensing data from the Atacama Cosmology Telescope, the new Type Ia supernova sample from the $\mathrm{Pantheon}+\text{survey}$, and baryonic acoustic oscillation (BAO) measurements from the Sloan Digital Sky Survey and the Dark Energy Spectroscopic Instrument. Using our fiducial data combination, described in the appendix, we find the neutrino mass limit is stable to most model extensions, with such extensions degrading the limit by less than 10%. We find a broadest bound of $\sum m_{\nu }<0.19\mathrm{eV}$at 95% confidence for a model with dynamical dark energy, although this scenario is not statistically preferred over the simpler $\mathrm{\Lambda }\mathrm{CDM}$model. 

Abstract We investigate how dust foreground complexity can affect measurements of the tensor-to-scalar ratio,r, in the context of the Simons Observatory, using a cross-spectrum component separation analysis. Employing a suite of simulations with realistic Galactic dust emission, we find that spatial variation in the dust frequency spectrum, parametrized byβ_d, can bias the estimate forrwhen modeled using a low-order moment expansion to capture this spatial variation. While this approach performs well across a broad range of dust complexity, the bias increases with more extreme spatial variation in dust frequency spectrum, reaching as high asr∼ 0.03 for simulations with no primordial tensors and a spatial dispersion ofσ(β_d) ≃ 0.3 — the most extreme case considered, yet still consistent with current observational constraints. This bias is driven by changes in the ℓ-dependence of the dust power spectrum as a function of frequency that can mimic a primordialB-mode tensor signal. Although low-order moment expansions fail to capture the full effect when the spatial variations ofβ_dbecome large and highly non-Gaussian, our results show that extended parametric methods can still recover unbiased estimates ofrunder a wide range of dust complexities. We further find that the bias inr, at the highest degrees of dust complexity, is largely insensitive to the spatial structure of the dust amplitude and is instead dominated by spatial correlations betweenβ_dand dust amplitude, particularly at higher orders. Ifβ_ddoes spatially vary at the highest levels investigated here, we would expect to use more flexible foreground models to achieve an unbiased constraint onrfor the noise levels anticipated from the Simons Observatory. 

ABSTRACT We use the emulation framework CosmoPower to construct and publicly release neural network emulators of cosmological observables, including the cosmic microwave background (CMB) temperature and polarization power spectra, matter power spectrum, distance-redshift relation, baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) observables, and derived parameters. We train our emulators on Einstein–Boltzmann calculations obtained with high-precision numerical convergence settings, for a wide range of cosmological models including ΛCDM, wCDM, ΛCDM + Neff, and ΛCDM + Σmν. Our CMB emulators are accurate to better than 0.5 per cent out to ℓ = 104, which is sufficient for Stage-IV data analysis, and our P(k) emulators reach the same accuracy level out to $$k=50 \, \, \mathrm{Mpc}^{-1}$$, which is sufficient for Stage-III data analysis. We release the emulators via an online repository (CosmoPower Organisation), which will be continually updated with additional extended cosmological models. Our emulators accelerate cosmological data analysis by orders of magnitude, enabling cosmological parameter extraction analyses, using current survey data, to be performed on a laptop. We validate our emulators by comparing them to class and camb and by reproducing cosmological parameter constraints derived from Planck TT, TE, EE, and CMB lensing data, as well as from the Atacama Cosmology Telescope Data Release 4 CMB data, Dark Energy Survey Year-1 galaxy lensing and clustering data, and Baryon Oscillation Spectroscopic Survey Data Release 12 BAO and RSD data. 

Large sinusoidal variations in the radio light curves of the blazars PKS J0805–0111 and PKS 2131–021 have recently been discovered with an 18-year monitoring programme at the Owens Valley Radio Observatory, making these systems strong supermassive black hole binary (SMBHB) candidates. The sinusoidal variations in PKS 2131–021 dominate its light curves from 2.7 GHz to optical frequencies. We report sinusoidal variations observed in both objects with the Atacama Cosmology Telescope (ACT) at 95, 147, and 225 GHz consistent with the radio light curves. The ACT 95 GHz light curve of PKS 2131–021 agrees well with the contemporaneous 91.5 GHz ALMA light curve and is comparable in quality, while the ACT light curves of PKS J0805–0111, for which there are no ALMA or other millimetre light curves, show that PKS 2131–021 is not an isolated case, and that this class of AGN exhibits the following properties: (a) the sinusoidal pattern dominates over a broad range of frequencies; (b) the amplitude of the sine wave compared to its mean value is monochromatic (i.e. nearly constant across frequencies); (c) the phase of the sinusoid phase changes monotonically as a function of frequency; (d) the sinusoidal variations are intermittent. We describe a physical model for SMBHB systems, the modified Kinetic Orbital model, that explains all four of these phenomena. The monitoring of ∼8000 blazars by the Simons Observatory over the next decade should provide a large number of SMBHB candidates that will shed light on the nature of the nanohertz gravitational-wave background. 

We present a joint analysis of the cosmic microwave background (CMB) lensing power spectra measured from the Data Release 6 of the Atacama Cosmology Telescope (ACT) and PR4, cross-correlations between the ACT and lensing reconstruction and galaxy clustering from unWISE, and the unWISE clustering auto-spectrum. We obtain 1.5% constraints on the matter density fluctuations at late times parametrized by the best constrained parameter combination $S_8^{3\mathrm{x}2\mathrm{pt}}\equiv {\sigma }_8({\mathrm{\Omega }}_m/0.3{)}^{0.4}=0.815\pm 0.012$. The commonly used $S_8\equiv {\sigma }_8({\mathrm{\Omega }}_m/0.3{)}^{0.5}$parameter is constrained to $S_8=0.816\pm 0.015$. In combination with baryon acoustic oscillation (BAO) measurements we find ${\sigma }_8=0.815\pm 0.012$. We also present sound-horizon-independent estimates of the present day Hubble rate of $H_0={66.4}_{-3.7}^{+3.2}\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$from our large scale structure data alone and $H_0={64.3}_{-2.4}^{+2.1}\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$in combination with uncalibrated supernovae from . Using parametric estimates of the evolution of matter density fluctuations, we place constraints on cosmic structure in a range of high redshifts typically inaccessible with cross-correlation analyses. Combining lensing cross- and autocorrelations, we derive a 3.3% constraint on the integrated matter density fluctuations above $z=2.4$, one of the tightest constraints in this redshift range and fully consistent with a $\mathrm{\Lambda }$cold dark matter ( $\mathrm{\Lambda }\mathrm{CDM}$) model fit to the primary CMB from . Finally, combining with primary CMB observations and using the extended low redshift coverage of these combined datasets we derive constraints on a variety of extensions to the $\mathrm{\Lambda }\mathrm{CDM}$model including massive neutrinos, spatial curvature, and dark energy. We find in flat $\mathrm{\Lambda }\mathrm{CDM}\sum m_{\nu }<0.12\mathrm{eV}$at 95% confidence using the large scale structure data, BAO measurements from Sloan Digital Sky Survey, and primary CMB observations. 

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale ofk_max = 0.4 Mpc^-1for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data. 

Abstract The Atacama Cosmology Telescope Data Release 6 (ACT DR6) power spectrum is expected to provide state-of-the-art cosmological constraints, with an associated need for precise error modeling. In this paper we design, and evaluate the performance of, an analytic covariance matrix prescription for the DR6 power spectrum that sufficiently accounts for the complicated ACT map properties. We use recent advances in the literature to handle sharp features in the signal and noise power spectra, and account for the effect of map-level anisotropies on the covariance matrix. In including inhomogeneous survey depth information, the resulting covariance matrix prescription is structurally similar to that used in thePlanckCosmic Microwave Background (CMB) analysis. We quantify the performance of our prescription using comparisons to Monte Carlo simulations, finding better than 3% agreement. This represents an improvement from a simpler, pre-existing prescription, which differs from simulations by ∼ 16%. We develop a new method to correct the analytic covariance matrix using simulations, after which both prescriptions achieve better than 1% agreement. This correction method outperforms a commonly used alternative, where the analytic correlation matrix is assumed to be accurate when correcting the covariance. Beyond its use for ACT, this framework should be applicable for future high resolution CMB experiments including the Simons Observatory (SO).