Unbiased likelihood-free inference of the Hubble constant from light standard sirens

Multi-messenger observations of binary neutron star mergers offer a promising path towards resolution of the Hubble constant ($H_0$) tension, provided their constraints are shown to be free from systematics such as the Malmquist bias. In the traditional Bayesian framework, accounting for selection effects in the likelihood requires calculation of the expected number (or fraction) of detections as a function of the parameters describing the population and cosmology; a potentially costly and/or inaccurate process. This calculation can, however, be bypassed completely by performing the inference in a framework in which the likelihood is never explicitly calculated, but instead fit using forward simulations of the data, which naturally include the selection. This is Likelihood-Free Inference (LFI). Here, we use density-estimation LFI, coupled to neural-network-based data compression, to infer $H_0$ from mock catalogues of binary neutron star mergers, given noisy redshift, distance and peculiar velocity estimates for each object. We demonstrate that LFI yields statistically unbiased estimates of $H_0$ in the presence of selection effects, with precision matching that of sampling the full Bayesian hierarchical model. Marginalizing over the bias increases the $H_0$ uncertainty by only $6%$ for training sets consisting of $O(10^4)$ populations. The resulting LFI framework is applicable to population-level inference problems with selection effects across astrophysics.

Prospects for Measuring the Hubble Constant with Neutron-Star-Black-Hole Mergers

Gravitational wave (GW) and electromagnetic (EM) observations of neutron-star-black-hole (NSBH) mergers can provide precise local measurements of the Hubble constant ($H_0$), ideal for resolving the current $H_0$ tension. We perform end-to-end analyses of realistic populations of simulated NSBHs, incorporating both GW and EM selection for the first time. We show that NSBHs could achieve unbiased 1.5-2.4% precision $H_0$ estimates by 2030. The achievable precision is strongly affected by the details of spin precession and tidal disruption, highlighting the need for improved modeling of NSBH mergers.

SSSpaNG! Stellar Spectra as Sparse, data-driven, Non-Gaussian processes

Upcoming million-star spectroscopic surveys have the potential to revolutionize our view of the formation and chemical evolution of the Milky Way. Realizing this potential requires automated approaches to optimize estimates of stellar properties, such as chemical element abundances, from the spectra. The volume and quality of the observations strongly motivate that these approaches should be data-driven. With this in mind, we introduce SSSpaNG: a data-driven non-Gaussian Process model of stellar spectra. We demonstrate the capabilities of SSSpaNG using a sample of APOGEE red clump stars, whose model parameters we infer via Gibbs sampling. Pooling information between stars to infer their covariance, we permit clear identification of the correlations between spectral pixels. Harnessing these correlations, we infer the true spectrum of each star, inpainting missing regions and denoising by a factor of at least 2 for stars with signal-to-noise of ~20. As we marginalize over the covariance matrix of the spectra, the effective prior on these true spectra is non-Gaussian and sparsifying, favouring typically small but occasionally large excursions from the mean. The high-fidelity inferred spectra produced will enable improved elemental abundance measurements for individual stars. Our model also allows us to quantify the information gained by observing portions of a stars spectrum, and thereby define the most mutually informative spectral regions. Using 25 windows centred on elemental absorption lines, we demonstrate that the iron-peak and alpha-process elements are particularly mutually informative for these spectra, and that the majority of information about a target window is contained in the 10-or-so most informative windows. Such mutual-information estimates have the potential to inform models of nucleosynthetic yields and the design of future observations.

Optimal proposals for Approximate Bayesian Computation

We derive the optimal proposal density for Approximate Bayesian Computation (ABC) using Sequential Monte Carlo (SMC) (or Population Monte Carlo, PMC). The criterion for optimality is that the SMC/PMC-ABC sampler maximise the effective number of samples per parameter proposal. The optimal proposal density represents the optimal trade-off between favoring high acceptance rate and reducing the variance of the importance weights of accepted samples. We discuss two convenient approximations of this proposal and show that the optimal proposal density gives a significant boost in the expected sampling efficiency compared to standard kernels that are in common use in the ABC literature, especially as the number of parameters increases.

Prospects for resolving the Hubble constant tension with standard sirens

The Hubble constant ($H_0$) estimated from the local Cepheid-supernova (SN) distance ladder is in 3-$sigma$ tension with the value extrapolated from cosmic microwave background (CMB) data assuming the standard cosmological model. Whether this tension represents new physics or systematic effects is the subject of intense debate. Here, we investigate how new, independent $H_0$ estimates can arbitrate this tension, assessing whether the measurements are consistent with being derived from the same model using the posterior predictive distribution (PPD). We show that, with existing data, the inverse distance ladder formed from BOSS baryon acoustic oscillation measurements and the Pantheon SN sample yields an $H_0$ posterior near-identical to the Planck CMB measurement. The observed local distance ladder value is a very unlikely draw from the resulting PPD. Turning to the future, we find that a sample of $sim50$ binary neutron star standard sirens (detectable within the next decade) will be able to adjudicate between the local and CMB estimates.

Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder

Estimates of the Hubble constant, $H_0$, from the distance ladder and the cosmic microwave background (CMB) differ at the $sim$3-$sigma$ level, indicating a potential issue with the standard $Lambda$CDM cosmology. Interpreting this tension correctly requires a model comparison calculation depending on not only the traditional `$n$-$sigma$ mismatch but also the tails of the likelihoods. Determining the form of the tails of the local $H_0$ likelihood is impossible with the standard Gaussian least-squares approximation, as it requires using non-Gaussian distributions to faithfully represent anchor likelihoods and model outliers in the Cepheid and supernova (SN) populations, and simultaneous fitting of the full distance-ladder dataset to correctly propagate uncertainties. We have developed a Bayesian hierarchical model that describes the full distance ladder, from nearby geometric anchors through Cepheids to Hubble-Flow SNe. This model does not rely on any distributions being Gaussian, allowing outliers to be modeled and obviating the need for arbitrary data cuts. Sampling from the $sim$3000-parameter joint posterior using Hamiltonian Monte Carlo, we find $H_0$ = (72.72 $pm$ 1.67) ${rm km,s^{-1},Mpc^{-1}}$ when applied to the outlier-cleaned Riess et al. (2016) data, and ($73.15 pm 1.78$) ${rm km,s^{-1},Mpc^{-1}}$ with SN outliers reintroduced. Our high-fidelity sampling of the low-$H_0$ tail of the distance-ladder likelihood allows us to apply Bayesian model comparison to assess the evidence for deviation from $Lambda$CDM. We set up this comparison to yield a lower limit on the odds of the underlying model being $Lambda$CDM given the distance-ladder and Planck XIII (2016) CMB data. The odds against $Lambda$CDM are at worst 10:1 or 7:1, depending on whether the SNe outliers are cut or modeled, or 60:1 if an approximation to the Planck Int. XLVI (2016) likelihood is used.

Cosmic Microwave Background Science at Commercial Airline Altitudes

Obtaining high-sensitivity measurements of degree-scale cosmic microwave background (CMB) polarization is the most direct path to detecting primordial gravitational waves. Robustly recovering any primordial signal from the dominant foreground emission will require high-fidelity observations at multiple frequencies, with excellent control of systematics. We explore the potential for a new platform for CMB observations, the Airlander 10 hybrid air vehicle, to perform this task. We show that the Airlander 10 platform, operating at commercial airline altitudes, is well-suited to mapping frequencies above 220 GHz, which are critical for cleaning CMB maps of dust emission. Optimizing the distribution of detectors across frequencies, we forecast the ability of Airlander 10 to clean foregrounds of varying complexity as a function of altitude, demonstrating its complementarity with both existing (Planck) and ongoing (C-BASS) foreground observations. This novel platform could play a key role in defining our ultimate view of the polarized microwave sky.

Robust forecasts on fundamental physics from the foreground-obscured, gravitationally-lensed CMB polarization

[Abridged] Recent results from the BICEP, Keck Array and Planck Collaborations demonstrate that Galactic foregrounds are an unavoidable obstacle in the search for evidence of inflationary gravitational waves in the cosmic microwave background (CMB) polarization. Beyond the foregrounds, the effect of lensing by intervening large-scale structure further obscures all but the strongest inflationary signals permitted by current data. With a plethora of ongoing and upcoming experiments aiming to measure these signatures, careful and self-consistent consideration of experiments foreground- and lensing-removal capabilities is critical in obtaining credible forecasts of their performance. We investigate the capabilities of instruments such as Advanced ACTPol, BICEP3 and Keck Array, CLASS, EBEX10K, PIPER, Simons Array, SPT-3G and SPIDER, and projects as COrE+, LiteBIRD-ext, PIXIE and Stage IV, to clean contamination due to polarized synchrotron and dust from raw multi-frequency data, and remove lensing from the resulting co-added CMB maps (either using iterative CMB-only techniques or through cross-correlation with external data). Incorporating these effects, we present forecasts for the constraining power of these experiments in terms of inflationary physics, the neutrino sector, and dark energy parameters. Made publicly available through an online interface, this tool enables the next generation of CMB experiments to foreground-proof their designs, optimize their frequency coverage to maximize scientific output, and determine where cross-experimental collaboration would be most beneficial. We find that analyzing data from ground, balloon and space instruments in complementary combinations can significantly improve component separation performance, delensing, and cosmological constraints over individual datasets.

Forecasting constraints from the cosmic microwave background on eternal inflation

We forecast the ability of cosmic microwave background (CMB) temperature and polarization datasets to constrain theories of eternal inflation using cosmic bubble collisions. Using the Fisher matrix formalism, we determine both the overall detectability of bubble collisions and the constraints achievable on the fundamental parameters describing the underlying theory. The CMB signatures considered are based on state-of-the-art numerical relativistic simulations of the bubble collision spacetime, evolved using the full temperature and polarization transfer functions. Comparing a theoretical cosmic-variance-limited experiment to the WMAP and Planck satellites, we find that there is no improvement to be gained from future temperature data, that adding polarization improves detectability by approximately 30%, and that cosmic-variance-limited polarization data offer only marginal improvements over Planck. The fundamental parameter constraints achievable depend on the precise values of the tensor-to-scalar ratio and energy density in (negative) spatial curvature. For a tensor-to-scalar ratio of $0.1$ and spatial curvature at the level of $10^{-4}$, using cosmic-variance-limited data it is possible to measure the width of the potential barrier separating the inflating false vacuum from the true vacuum down to $M_{rm Pl}/500$, and the initial proper distance between colliding bubbles to a factor $pi/2$ of the false vacuum horizon size (at three sigma). We conclude that very near-future data will have the final word on bubble collisions in the CMB.

Sparse Inpainting and Isotropy

Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.