Dataset Information

Hubble parameter estimation via dark sirens with the LISA-Taiji network.

ABSTRACT: The Hubble parameter is one of the central parameters in modern cosmology, and describes the present expansion rate of the universe. The values of the parameter inferred from late-time observations are systematically higher than those inferred from early-time measurements by about [Formula: see text]. To reach a robust conclusion, independent probes with accuracy at percent levels are crucial. Gravitational waves from compact binary coalescence events can be formulated into the standard siren approach to provide an independent Hubble parameter measurement. The future space-borne gravitational wave observatory network, such as the LISA-Taiji network, will be able to measure the gravitational wave signals in the millihertz bands with unprecedented accuracy. By including several statistical and instrumental noises, we show that, within a five-year operation time, the LISA-Taiji network is able to constrain the Hubble parameter within [Formula: see text] accuracy, and possibly beats the scatters down to [Formula: see text] or even better.

SUBMITTER: Wang R

PROVIDER: S-EPMC8863402 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Hubble parameter estimation via dark sirens with the LISA-Taiji network.

Wang Renjie R Ruan Wen-Hong WH Yang Qing Q Guo Zong-Kuan ZK Cai Rong-Gen RG Hu Bin B

National science review 20210401 2

The Hubble parameter is one of the central parameters in modern cosmology, and describes the present expansion rate of the universe. The values of the parameter inferred from late-time observations are systematically higher than those inferred from early-time measurements by about [Formula: see text]. To reach a robust conclusion, independent probes with accuracy at percent levels are crucial. Gravitational waves from compact binary coalescence events can be formulated into the standard siren ap ...[more]

PMID: 35211320

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Project description:Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants.We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation.A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission.

Dataset Information

Hubble parameter estimation via dark sirens with the LISA-Taiji network.

Publications

Hubble parameter estimation via dark sirens with the LISA-Taiji network.

Similar Datasets

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