Cosmological Parameter Estimation Research Articles

H i Intensity Mapping Cross-correlation with Thermal Sunyaev–Zel’dovich Fluctuations: Forecasted Cosmological Parameter Estimation for FAST and Planck

The 21 cm emission from neutral hydrogen surveys holds great potential as a valuable method for exploring the large-scale structure (LSS) of the Universe. In this paper, we forecast for the cross-correlation between the thermal Sunyaev–Zel’dovich (SZ) fluctuations as probed by the Planck satellite and fluctuations in the H i brightness temperature as probed by the ground-based Five-hundred-meter Aperture Spherical Telescope to trace the connection between galaxy clusters and the H i LSS. Assuming that the measurement is limited by instrumental noise rather than by foreground, we estimate the potential detectability of the cross-correlation signal and the improvement in the measurement of the H i cosmic density, the hydrostatic mass bias parameter, and the universal pressure profile parameters. We obtain a constraint on the cosmic neutral hydrogen density parameter significantly to σ(ΩH I) = 1.0 × 10−6. We also find that the average halo masses contributing to the H i − y cross-power spectrum in the one-halo regime is ∼1.5 × 1014 M ⊙. Our results also show that the H i–SZ cross-correlation has great potential to probe the distribution of neutral hydrogen (H i) within halos at low redshift.

Variational inference for acceleration of SN Ia photometric distance estimation with BayeSN

ABSTRACT Type Ia supernovae (SNe Ia) are standarizable candles whose observed light curves can be used to infer their distances, which can in turn be used in cosmological analyses. As the quantity of observed SNe Ia grows with current and upcoming surveys, increasingly scalable analyses are necessary to take full advantage of these new data sets for precise estimation of cosmological parameters. Bayesian inference methods enable fitting SN Ia light curves with robust uncertainty quantification, but traditional posterior sampling using Markov Chain Monte Carlo (MCMC) is computationally expensive. We present an implementation of variational inference (VI) to accelerate the fitting of SN Ia light curves using the BayeSN hierarchical Bayesian model for time-varying SN Ia spectral energy distributions. We demonstrate and evaluate its performance on both simulated light curves and data from the Foundation Supernova Survey with two different forms of surrogate posterior–a multivariate normal and a custom multivariate zero-lower-truncated normal distribution–and compare them with the Laplace Approximation and full MCMC analysis. To validate of our variational approximation, we calculate the Pareto-smoothed importance sampling diagnostic, and perform variational simulation-based calibration. The VI approximation achieves similar results to MCMC but with an order-of-magnitude speed-up for the inference of the photometric distance moduli. Overall, we show that VI is a promising method for scalable parameter inference that enables analysis of larger data sets for precision cosmology.

Strong-lensing cosmography using third-generation gravitational-wave detectors

Abstract We present a detailed exposition of a statistical method for estimating cosmological parameters from the observation of a large number of strongly lensed binary-black-hole (BBH) mergers observable by next (third) generation (XG) gravitational-wave (GW) detectors. This method, first presented in [1], compares the observed number of strongly lensed GW events and their time delay distribution (between lensed images) with observed events to infer cosmological parameters. We show that the precision of the estimation of the cosmological parameters does not have a strong dependance on the assumed BBH redshift distribution model. Using the large number of unlensed mergers, XG detectors are expected to measure the BBH redshift distribution with sufficient precision for the cosmological inference. However, a biased inference of the BBH redshift distribution will bias the estimation of cosmological parameters. An incorrect model for the distribution of lens properties can also lead to a biased cosmological inference. However, Bayesian model selection can assist in selecting the right model from a set of available parametric models for the lens distribution. We also present a way to incorporate the effect of contamination in the data due to the limited efficiency of lensing identification methods, so that it will not bias the cosmological inference.

Catalog-level blinding on the bispectrum for DESI-like galaxy surveys

We evaluate the performance of the catalog-level blind analysis technique (blinding) presented in Brieden et al. (2020) in the context of a fixed template power spectrum and bispectrum analysis. This blinding scheme, which is tailored for galaxy redshift surveys similar to the Dark Energy Spectroscopic Instrument (DESI), has two components: the so-called “AP blinding” (concerning the dilation parameters α ∥, α ⊥) and “RSD blinding” (redshift space distortions, affecting the growth rate parameter f). Through extensive testing, including checks for the RSD part in cubic boxes, the impact of AP blinding on mocks with realistic survey sky coverage, and the implementation of a full AP+RSD blinding pipeline, our analysis demonstrates the effectiveness of the technique in preserving the integrity of cosmological parameter estimation when the analysis includes the bispectrum statistic. We emphasize the critical role of sophisticated — and difficult to accidentally unblind — blinding methods in precision cosmology.

Improved weak lensing photometric redshift calibration via StratLearn and hierarchical modelling

ABSTRACT Discrepancies between cosmological parameter estimates from cosmic shear surveys and from recent Planck cosmic microwave background measurements challenge the ability of the highly successful $\Lambda$CDM model to describe the nature of the Universe. To rule out systematic biases in cosmic shear survey analyses, accurate redshift calibration within tomographic bins is key. In this paper, we improve photo-z calibration via Bayesian hierarchical modeling of full galaxy photo-z conditional densities, by employing ${\it StratLearn}$, a recently developed statistical methodology, which accounts for systematic differences in the distribution of the spectroscopic training/source set and the photometric target set. Using realistic simulations that were designed to resemble the KiDS + VIKING-450 data set, we show that ${\it StratLearn}$-estimated conditional densities improve the galaxy tomographic bin assignment, and that our ${\it StratLearn}$-Bayesian framework leads to nearly unbiased estimates of the target population means. This leads to a factor of $\sim 2$ improvement upon often used and state-of-the-art photo-z calibration methods. Our approach delivers a maximum bias per tomographic bin of $\Delta \langle z \rangle = 0.0095 \pm 0.0089$, with an average absolute bias of $0.0052 \pm 0.0067$ across the five tomographic bins.

Are light curve classification metrics good proxies for SN Ia cosmological constraining power?

When selecting a lc classifier for use as part of a photometric supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, such as the contamination within the photometrically classified SN Ia sample, rather than a measure of cosmological constraining power. If the former is an appropriate proxy for the latter, this practice would save those designing an analysis pipeline from eliminate the computational expense of a full cosmology forecast in the analysis pipeline design process This study tests the assumption that lc classification metrics are an appropriate proxy for cosmology metrics. We emulated photometric SN Ia cosmology lc samples with controlled contamination rates of individual contaminant classes and evaluated each of them under a set of classification metrics. We then derived cosmological parameter constraints from all samples under two common analysis approaches and quantified the impact of contamination by each contaminant class on the resulting cosmological parameter estimates. We observe that cosmology metrics are sensitive to both the contamination rate and the class of the contaminating population, whereas the classification metrics are shown to be insensitive to the latter. Based on these findings, we discourage any exclusive reliance on classification-based metrics for analysis design decisions, which (counterintuitively) include but are not limited to the classifier choice. Instead, we recommend optimising science analysis pipeline design choices using a metric of the information gained about the physical parameters of interest.

Full shape cosmology analysis from BOSS in configuration space using neural network acceleration

Recently, a new wave of full modeling analyses have emerged within the Large-Scale Structure community, leading mostly to tighter constraints on the estimation of cosmological parameters, when compared with standard approaches used over the last decade by collaboration analyses of stage III experiments. However, the majority of these full-shape analyses have primarily been conducted in Fourier space, with limited emphasis on exploring the configuration space. Investigating n-point correlations in configuration space demands a higher computational cost compared to Fourier space because it typically requires an additional integration step. This can pose a limitation when using these approaches, especially when considering higher-order statistics. One avenue to mitigate the high computation time is to take advantage of neural network acceleration techniques. In this work, we present a full shape analysis of Sloan Digital Sky Survey III/BOSS in configuration space using a neural network accelerator. We show that the efficacy of the pipeline is enhanced by a time factor 103 without sacrificing precision, making it possible to reduce the error associated with the surrogate modeling to below 10-2 percent which is compatible with the precision required for current stage IV experiments such as DESI. We find Ω m = 0.286±0.009, H 0 = 68.8±1.2 kms-1Mpc-1 and A s × 109 = 2.09 +0.25 -0.29. Our results on public BOSS data are in good agreement with BOSS official results and compatible with other independent full modeling analyses. We explore relaxing the prior on ωb and varying ns , without significant changes in the mean values of the cosmological parameters posterior distributions, but enlarging their widths. Finally, we explore the information content of the multipoles when constraining cosmological parameters.

Standard siren cosmology in the era of the 2.5-generation ground-based gravitational wave detectors: bright and dark sirens of LIGO Voyager and NEMO

The 2.5-generation (2.5G) ground-based gravitational wave (GW) detectors LIGO Voyager and NEMO are expected to be operational in the late 2020s and early 2030s. In this work, we explore the potential of GW standard sirens observed by the 2.5G GW detectors in measuring cosmological parameters, especially for the Hubble constant. Using GWs to measure cosmological parameters is inherently challenging, especially for 2.5G detectors, given their limited capability, which results in weaker constraints on cosmological parameters from the detected standard sirens. However, the measurement of the Hubble constant using standard siren observations from Voyager and NEMO is still promising. For example, using bright sirens from Voyager and NEMO can measure the Hubble constant with a precision of about 2% and 6% respectively, and using the Voyager-NEMO network can improve the precision to about 1.6%. Moreover, bright sirens can be used to break the degeneracy of cosmological parameters generated by CMB data, and to a certain extent, 2.5G detectors can also play a role in this aspect. Observations of dark sirens by 2.5G detectors can achieve relatively good results in measuring the Hubble constant, with a precision of within 2%, and if combining observations of bright and dark sirens, the precision of the Hubble constant measurement can reach about 1.4%. Finally, we also discussed the impact of the uncertainty in the binary neutron star merger rate on the estimation of cosmological parameters. We conclude that the magnificent prospect for solving the Hubble tension is worth expecting in the era of the 2.5G ground-based GW detectors.

A comprehensive forecast for cosmological parameter estimation using joint observations of gravitational waves and short γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document}-ray bursts

In the third-generation (3G) gravitational-wave (GW) detector era, multi-messenger GW observation for binary neutron star (BNS) merger events can exert great impacts on exploring cosmic expansion history. In this work, we comprehensively explore the potential of 3G GW standard siren observations in cosmological parameter estimations by considering 3G GW detectors and the future short γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document}-ray burst (GRB) detector THESEUS-like telescope joint observations. Based on a 10-year observation of different detection strategies, we predict that the numbers of detectable GW-GRB events are 334–674 with the redshifts z<3.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z<3.5$$\\end{document} and the inclination angles ι<15∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\iota <15^{\\circ }$$\\end{document}. For the cosmological analysis, we consider the Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Lambda $$\\end{document}CDM, wCDM, w0wa\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$w_0w_a$$\\end{document}CDM, and interacting dark energy (IDE) models. We find that GW can tightly constrain the Hubble constant with precision of 0.345–0.065%, but does not perform well in constraining other cosmological parameters. Fortunately, GW detection could effectively break the cosmological parameter degeneracies generated by the mainstream EM observations, CMB + BAO + SN (CBS). When combining mock GW data with CBS data, CBS + GW can tightly constrain the equation of state of dark energy w with a precision of 1.26%, close to the standard of precision cosmology. Meanwhile, the addition of GW to CBS could improve constraints on cosmological parameters by 34.2–94.9%. In conclusion, GW standard siren observations from 3G GW detectors could play a crucial role in helping solve the Hubble tension and probe the fundamental nature of dark energy.

Tensions in cosmology: A discussion of statistical tools to determine inconsistencies

We present a comprehensive analysis of statistical tools for evaluating tensions in cosmological parameter estimates arising from distinct datasets. Focusing on the unresolved Hubble constant (H0) tension, we explore the Pantheon Plus + SH0ES (PPS) compilation, which includes low-redshift Cepheid data from the SH0ES collaboration, along with the latest release of CMB data from the Planck collaboration, Cosmic Chronometers (CC) dataset and the most recent Baryonic Acoustic Oscillation (BAO) datasets. Employing various tension metrics, we quantitatively assess the inconsistencies in parameter estimates, emphasizing the importance of capturing multidimensional tensions. Our results reveal substantial tension between PPS and Planck 2018 datasets and moderate tension between the BAO data sets and all other datasets. We highlight the importance of adopting these metrics to enhance the precision of future cosmological analyses and facilitate the resolution of existing tensions.

Euclid preparation

In this paper we investigate the impact of lensing magnification on the analysis of Euclid’s spectroscopic survey using the multipoles of the two-point correlation function for galaxy clustering. We determine the impact of lensing magnification on cosmological constraints as well as the expected shift in the best-fit parameters if magnification is ignored. We considered two cosmological analyses: (i) a full-shape analysis based on the Λ cold dark matter (CDM) model and its extension w0waCDM and (ii) a model-independent analysis that measures the growth rate of structure in each redshift bin. We adopted two complementary approaches in our forecast: the Fisher matrix formalism and the Markov chain Monte Carlo method. The fiducial values of the local count slope (or magnification bias), which regulates the amplitude of the lensing magnification, have been estimated from the Euclid Flagship simulations. We used linear perturbation theory and modelled the two-point correlation function with the public code coffe. For a ΛCDM model, we find that the estimation of cosmological parameters is biased at the level of 0.4–0.7 standard deviations, while for a w0waCDM dynamical dark energy model, lensing magnification has a somewhat smaller impact, with shifts below 0.5 standard deviations. For a model-independent analysis aimed at measuring the growth rate of structure, we find that the estimation of the growth rate is biased by up to 1.2 standard deviations in the highest redshift bin. As a result, lensing magnification cannot be neglected in the spectroscopic survey, especially if we want to determine the growth factor, one of the most promising ways to test general relativity with Euclid. We also find that, by including lensing magnification with a simple template, this shift can be almost entirely eliminated with minimal computational overhead.

Analytical Gaussian process cosmography: unveiling insights into matter-energy density parameter at present

In this study, we introduce a novel analytical Gaussian Process (GP) cosmography methodology, leveraging the differentiable properties of GPs to derive key cosmological quantities analytically. Our approach combines cosmic chronometer (CC) Hubble parameter data with growth rate (f) observations to constrain the Ωm0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{\ extrm{m0}}$$\\end{document} parameter, offering insights into the underlying dynamics of the Universe. By formulating a consistency relation independent of specific cosmological models, we analyze under a flat FLRW metric and first-order Newtonian perturbation theory framework. Our analytical approach simplifies the process of Gaussian Process regression (GPR), providing a more efficient means of handling large datasets while offering deeper interpretability of results. We demonstrate the effectiveness of our methodology by deriving precise constraints on Ωm0h2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{\ extrm{m0}}h^2$$\\end{document}, revealing Ωm0h2=0.139±0.017\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _\ extrm{m0}h^2=0.139\\pm 0.017$$\\end{document}. Moreover, leveraging H0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H_0$$\\end{document} observations, we further constrain Ωm0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{\ extrm{m0}}$$\\end{document}, uncovering an inverse correlation between mean H0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H_0$$\\end{document} and Ωm0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{\ extrm{m0}}$$\\end{document}. Our investigation offers a proof of concept for analytical GP cosmography, highlighting the advantages of analytical methods in cosmological parameter estimation.

Euclidpreparation

This work considers which higher order modeling effects on the cosmic shear angular power spectra must be taken into account forEuclid. We identified the relevant terms and quantified their individual and cumulative impact on the cosmological parameter inferences fromEuclid. We computed the values of these higher order effects using analytic expressions and calculated the impact on cosmological parameter estimations using the Fisher matrix formalism. We reviewed 24 effects and determined the ones that potentially need to be accounted for, namely: the reduced shear approximation, magnification bias, source-lens clustering, source obscuration, local Universe effects, and the flat Universe assumption. After computing these effects explicitly and calculating their cosmological parameter biases, using a maximum multipole ofℓ = 5000, we find that the magnification bias, source-lens clustering, source obscuration, and local Universe terms individually produce significant (&gt; 0.25σ) cosmological biases in one or more parameters; accordingly, these effects must be accounted for and warrant further investigation. In total, we find biases in Ωm, Ωb,h, andσ8of 0.73σ, 0.28σ, 0.25σ, and −0.79σ, respectively, for the flat ΛCDM. For thew0waCDM case, we found biases in Ωm, Ωb,h,ns,σ8, andwaof 1.49σ, 0.35σ, −1.36σ, 1.31σ, −0.84σ, and −0.35σ, respectively. These are increased relative to the ΛCDM due to additional degeneracies as a function of redshift and scale.

Constraining primordial non-Gaussianity using neural networks

ABSTRACT We present a novel approach to estimate the value of primordial non-Gaussianity (fNL) parameter directly from the cosmic microwave background (CMB) maps using a convolutional neural network (CNN). While traditional methods rely on complex statistical techniques, this study proposes a simpler approach that employs a neural network to estimate fNL. The neural network model is trained on simulated CMB maps with known fNL in range of [−50, 50], and its performance is evaluated using various metrics. The results indicate that the proposed approach can accurately estimate fNL values from CMB maps with a significant reduction in complexity compared to traditional methods. With 500 validation data, the $f^{\rm output}_{\rm NL}$ against $f^{\rm input}_{\rm NL}$ graph can be fitted as y = ax + b, where $a=0.980^{+0.098}_{-0.102}$ and $b=0.277^{+0.098}_{-0.101}$, indicating the unbiasedness of the primordial non-Gaussianity estimation. The results suggest that the CNN technique can be widely applied to other cosmological parameter estimation directly from CMB images.

FkPT: constraining scale-dependent modified gravity with the full-shape galaxy power spectrum

Modified gravity models with scale-dependent linear growth typically exhibit an enhancement in the power spectrum beyond a certain scale. The conventional methods for extracting cosmological information usually involve inferring modified gravity effects via Redshift Space Distortions (RSD), particularly through the time evolution of fσ 8. However, classical galaxy RSD clustering analyses encounter difficulties in accurately capturing the spectrum's enhanced power, which is better obtained from the broad-band power spectrum. In this sense, full-shape analyses aim to consider survey data using comprehensive and precise models of the whole power spectrum. Yet, a major challenge in this approach is the slow computation of non-linear loop integrals for scale-dependent modified gravity, precluding the estimation of cosmological parameters using Markov Chain Monte Carlo methods. Based on recent studies, in this work we develop a perturbation theory tailored for Modified Gravity, or analogous scenarios introducing additional scales, such as in the presence of massive neutrinos. Our approach only needs the calculation of the scale-dependent growth rate f(k,t) and the limit of the perturbative kernels at large scales. We called this approximate technique as fk-Perturbation Theory and implemented it into the code fkpt, capable of computing the redshift space galaxy power spectrum in a fraction of a second. We validate our modeling and code with the f(R) theory MG-GLAM and General Relativity NSeries sets of simulations. The code is available at https://github.com/alejandroaviles/fkpt.

Cosmological Parameter Estimation with Genetic Algorithms

Genetic algorithms are a powerful tool in optimization for single and multimodal functions. This paper provides an overview of their fundamentals with some analytical examples. In addition, we explore how they can be used as a parameter estimation tool in cosmological models to maximize the likelihood function, complementing the analysis with the traditional Markov chain Monte Carlo methods. We analyze that genetic algorithms provide fast estimates by focusing on maximizing the likelihood function, although they cannot provide confidence regions with the same statistical meaning as Bayesian approaches. Moreover, we show that implementing sharing and niching techniques ensures an effective exploration of the parameter space, even in the presence of local optima, always helping to find the global optima. This approach is invaluable in the cosmological context, where an exhaustive space exploration of parameters is essential. We use dark energy models to exemplify the use of genetic algorithms in cosmological parameter estimation, including a multimodal problem, and we also show how to use the output of a genetic algorithm to obtain derived cosmological functions. This paper concludes that genetic algorithms are a handy tool within cosmological data analysis, without replacing the traditional Bayesian methods but providing different advantages.

Joint cosmological and gravitational-wave population inference using dark sirens and galaxy catalogues

In the absence of numerous gravitational-wave detections with confirmed electromagnetic counterparts, the “dark siren” method has emerged as a leading technique of gravitational-wave cosmology. The method allows redshift information of such events to be inferred statistically from a catalogue of potential host galaxies. Due to selection effects, dark siren analyses necessarily depend on the mass distribution of compact objects and the evolution of their merger rate with redshift. Informative priors on these quantities will impact the inferred posterior constraints on the Hubble constant (H 0). It is thus crucial to vary these unknown distributions during an H 0 inference. This was not possible in earlier analyses due to the high computational cost, restricting them to either excluding galaxy catalogue information, or fixing the gravitational-wave population mass distribution and risking introducing bias to the H 0 measurement. This paper introduces a significantly enhanced version of the Python package gwcosmo, which allows joint estimation of cosmological and compact binary population parameters. This thereby ensures the analysis is now robust to a major source of potential bias. The gravitational-wave events from the Third Gravitational-Wave Transient Catalogue are reanalysed with the GLADE+ galaxy catalogue, and an updated, more reliable measurement of H 0 = 69+12 -7 km s-1 Mpc-1 is found (maximum a posteriori probability and 68% highest density interval). This improved method will enable cosmological analyses with future gravitational-wave detections to make full use of the information available (both from galaxy catalogues and the compact binary population itself), leading to promising new independent bounds on the Hubble constant.

Fast and effortless computation of profile likelihoods using CONNECT

Abstract The frequentist method of profile likelihoods has recently received renewed attention in the field of cosmology. This is because the results of inferences based on the latter may differ from those of Bayesian inferences, either because of prior choices or because of non-Gaussianity in the likelihood function. Consequently, both methods are required for a fully nuanced analysis. However, in the last decades, cosmological parameter estimation has largely been dominated by Bayesian statistics due to the numerical complexity of constructing profile likelihoods, arising mainly from the need for a large number of gradient-free optimisations of the likelihood function. In this paper, we show how to accommodate the computational requirements of profile likelihoods using the publicly available neural network framework connect together with a novel modification of the gradient-based basin-hopping optimisation algorithm. Apart from the reduced evaluation time of the likelihood due to the neural network, we also achieve an additional speed-up of 1–2 orders of magnitude compared to profile likelihoods computed with the gradient-free method of simulated annealing, with excellent agreement between the two. This allows for the production of typical triangle plots normally associated with Bayesian marginalisation within cosmology (and previously unachievable using likelihood maximisation because of the prohibitive computational cost). We have tested the setup on three cosmological models: the ΛCDM model, an extension with varying neutrino mass, and finally a decaying cold dark matter model. Given the default precision settings in connect, we achieve a high precision in χ2 with a difference to the results obtained by class of Δχ2 ≈ 0.2 (and, importantly, without any bias in inferred parameter values) — easily good enough for profile likelihood analyses.

COSMOGLOBE: Towards end-to-end CMB cosmological parameter estimation without likelihood approximations

We implement support for a cosmological parameter estimation algorithm in Commander and quantify its computational efficiency and cost. For a semi-realistic simulation similar to Planck LFI 70 GHz, we find that the computational cost of producing one single sample is about 20 CPU-hours and that the typical Markov chain correlation length is ∼100 samples. The net effective cost per independent sample is ∼2000 CPU-hours, in comparison with all low-level processing costs of 812 CPU-hours for Planck LFI and WMAP in COSMOGLOBE Data Release 1. Thus, although technically possible to run already in its current state, future work should aim to reduce the effective cost per independent sample by one order of magnitude to avoid excessive runtimes, for instance through multi-grid preconditioners and/or derivative-based Markov chain sampling schemes. This work demonstrates the computational feasibility of true Bayesian cosmological parameter estimation with end-to-end error propagation for high-precision CMB experiments without likelihood approximations, but it also highlights the need for additional optimizations before it is ready for full production-level analysis.

Leveraging Transformers for Cosmological Parameter Estimation from a Large Scale Structure

Leveraging Transformers for Cosmological Parameter Estimation from a Large Scale Structure