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Register a new userReconstruction of the dark sectors interaction: A model-independent inference and forecast from GW standard sirens
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Alexander Bonilla Rivera
Publication date
2021
fields
Physics
and research's language is
English
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Interacting dark matter (DM) - dark energy (DE) models have been intensively investigated in the literature for their ability to fit various data sets as well as to explain some observational tensions persisting within the $Lambda$CDM cosmology. In this work, we employ Gaussian processes (GP) algorithm to perform a joint analysis by using the geometrical cosmological probes such as Cosmic chronometers, Supernova Type Ia, Baryon Acoustic Oscillations and the H0LiCOW lenses sample to infer a reconstruction of the coupling function between the dark components in a general framework, where the DE can assume a dynamical character via its equation of state. In addition to the joint analysis with these data, we simulate a catalog with standard siren events from binary neutron star mergers, within the sensitivity predicted by the Einstein Telescope, to reconstruct the dark sector coupling with more accuracy in a robust way. We find that the particular case, where $w = -1$ is fixed on the DE nature, has a statistical preference for an interaction in the dark sector at late times. In the general case, where $w(z)$ is analyzed, we find no evidence for such dark coupling, and the predictions are compatible with the $Lambda$CDM paradigm. When the mock events of the standard sirens are considered to improve the kernel in GP predictions, we find preference for an interaction in the dark sector at late times.
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The purpose of this work is to investigate the prospects of using the future standard siren data without redshift measurements to constrain cosmological parameters. With successful detections of gravitational wave (GW) signals an era of GW astronomy has begun. Unlike the electromagnetic domain, GW signals allow direct measurements of luminosity distances to the sources, while their redshifts remain to be measured by identifying electromagnetic counterparts. This leads to significant technical problems for almost all possible BH-BH systems. It is the major obstacle to cosmological applications of GW standard sirens. In this paper, we introduce the general framework of using luminosity distances alone for cosmological inference. The idea is to use the prior knowledge of the redshift probability distribution for coalescing sources from the intrinsic merger rates assessed with population synthesis codes. Then the posterior probability distributions for cosmological parameters can be calculated. We demonstrate the performance of our method on the simulated mock data and show that the luminosity distance measurement would enable an accurate determination of cosmological parameters up to $20%$ uncertainty level. We also find that in order to infer $H_0$ to 1% level with flat $Lambda$CDM model, we need about $10^5$ events.
Gravitational Waves (GWs) can determine the luminosity distance of the progenitor directly from the amplitude of the wave, without assuming any specific cosmological model. Thus, it can be considered as a standard siren. The coalescence of binary neutron stars (BNS) or neutron star-black hole pair (NSBH) can generate GWs as well as the electromagnetic counterpart, which can be detected in a form of Gamma-Ray Bursts (GRB) and can be used to determine the redshift of the source. Consequently, such a standard siren can be a very useful probe to constrain the cosmological parameters. In this work, we consider an interacting Dark Matter-Dark Energy (DM-DE) model. Assuming some fiducial values for the parameters of our model, we simulate the luminosity distance for a realistic and optimistic GW+GRB events , which can be detected by the third-generation GW detector Einstein Telescope (ET). Using these simulated events, we perform a Monte Carlo Markov Chain (MCMC) to constrain the DM-DE coupling constant and other model parameters in $1sigma$ and $2sigma$ confidence levels. We also investigate how GWs can improve the constraints obtained by current cosmological probes.
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.
Relaxing the conventional assumption of a minimal coupling between the dark matter (DM) and dark energy (DE) fields introduces significant changes in the predicted evolution of the Universe. Therefore, testing such a possibility constitutes an essential task not only for cosmology but also for fundamental physics. In a previous communication [Phys. Rev. D99, 043521, 2019], we proposed a new null test for the $Lambda$CDM model based on the time dependence of the ratio between the DM and DE energy densities which is also able to detect potential signatures of interaction between the dark components. In this work, we extend that analysis avoiding the $ Lambda$CDM assumption and reconstruct the interaction in the dark sector in a fully model-independent way using data from type Ia supernovae, cosmic chronometers and baryonic acoustic oscillations. According to our analysis, the $Lambda$CDM model is consistent with our model-independent approach at least at $3sigma$ CL over the entire range of redshift studied. On the other hand, our analysis shows that the current background data do not allow us to rule out the existence of an interaction in the dark sector. Finally, we present a forecast for next-generation LSS surveys. In particular, we show that Euclid and SKA will be able to distinguish interacting models with about 4% of precision at $zapprox 1$.
Model independent reconstructions of dark energy have received some attention. The approach that addresses the reconstruction of the dimensionless coordinate distance and its two first derivatives using a polynomial fit in different redshift windows is well developed cite{DalyDjorgovski1,DalyDjorgovski2,DalyDjorgovski3}. In this work we offer new insights into the problem by focusing on two types of observational probes: SNeIa and GRBs. Our results allow to highlight some of the intrinsic weaknesses of the method. One of the directions we follow is to consider updated observational samples. Our results indicate than conclusions on the main dark energy features as drawn from this method are intimately related to the features of the samples themselves (which are not quite ideal). This is particularly true of GRBs, which manifest themselves as poor performers in this context. In contrast to original works, we conclude they cannot be used for cosmological purposes, and the state of the art does not allow to regard them on the same quality basis as SNeIa. The next direction we contribute to is the question of how the adjusting of some parameters (window width, overlap, selection criteria) affect the results. We find again there is a considerable sensitivity to these features. Then, we try to establish what is the current redshift range for which one can make solid predictions on dark energy evolution. Finally, we strengthen the former view that this model is modest in the sense it provides only a picture of the global trend. But, on the other hand, we believe it offers an interesting complement to other approaches given that it works on minimal assumptions.
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