Multi-tasking the growth of cosmological structures

Pèrenon, Louis; Martinelli, Matteo; Ilić, Stéphane; Maartens, Roy; Lochner, Michelle; Clarkson, Chris

doi:10.1016/j.dark.2021.100898

Cited by 9 publications

(6 citation statements)

References 51 publications

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“…By contrast, reconstructions with future galaxy survey data do not suffer from this, see e.g. [22], and we will exploit this in section 4.…”

Section: Reconstructions With Current Datamentioning

confidence: 99%

“…We find that this precision on the data yields GP reconstructions much less dependent on the choices of kernel and priors than for current data. The reconstructions are now completely driven by the data [22]. We therefore choose an SE kernel and a null mean prior, as in [22].…”

Section: Reconstructing W Bg and W Grmentioning

confidence: 99%

“…The reconstructions are now completely driven by the data [22]. We therefore choose an SE kernel and a null mean prior, as in [22]. We find that only 2.7% of the reconstructions are rejected by the Ω m (z) < 1 condition.…”

Section: Reconstructing W Bg and W Grmentioning

confidence: 99%

“…With these measurements in hand, one can avoid reconstructing only f by combining the information contained in the measurements of the three functions. This can be done by reconstructing simultaneously f , σ 8 and f σ 8 via the use of multi-task GP, which greatly improves the error on the prediction of each quantity [22]. Following the procedure of [22], we use mock data to forecast σ 8 and f σ 8 , using the same prescriptions as we have for f , and thus reconstruct simultaneously the three functions.…”

Section: Reconstructing W Bg and W Grmentioning

confidence: 99%

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Measuring dark energy with expansion and growth

Pèrenon¹,

Martinelli²,

Maartens³

et al. 2022

Preprint

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We combine cosmic chronometer and growth of structure data to derive the redshift evolution of the dark energy equation of state w, using a novel agnostic approach. The background and perturbation equations lead to two expressions for w, one purely background-based and the other relying also on the growth rate of large-scale structure. We compare the features and performance of the growth-based w to the background w, using Gaussian Processes for the reconstructions. We find that current data is not precise enough for robust reconstruction of the two forms of w. By using mock data expected from next-generation surveys, we show that the reconstructions will be robust enough and that the growth-based w will out-perform the background w. Furthermore, any disagreement between the two forms of w will provide a new test for deviations from the standard model of cosmology.

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“…By contrast, reconstructions with future galaxy survey data do not suffer from this, see e.g. [22], and we will exploit this in section 4.…”

Section: Reconstructions With Current Datamentioning

confidence: 99%

Section: Reconstructing W Bg and W Grmentioning

confidence: 99%

Section: Reconstructing W Bg and W Grmentioning

confidence: 99%

Section: Reconstructing W Bg and W Grmentioning

confidence: 99%

See 2 more Smart Citations

Measuring dark energy with expansion and growth

Pèrenon¹,

Martinelli²,

Maartens³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method is often used when the function of interest is directly measured at certain redshifts. These measurements are used as a training sample of the GP model, which then can predict the values of the reconstructed function at redshifts lacking any direct measurements (Belgacem et al 2020;Renzi et al 2020;Mukherjee & Mukherjee 2021;Said et al 2021;Perenon et al 2021).…”

Section: Reconstruction Pipelinementioning

confidence: 99%

Learning how to surf: Reconstructing the propagation and origin of gravitational waves with Gaussian Processes

Cañas-Herrera,

Contigiani,

Vardanyan

2021

Preprint

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Soon, the combination of electromagnetic and gravitational signals will open the door to a new era of gravitational-wave (GW) cosmology. It will allow us to test the propagation of tensor perturbations across cosmic time and study the distribution of their sources over large scales. In this work, we show how machine learning techniques can be used to reconstruct new physics by leveraging the spatial correlation between GW mergers and galaxies. We explore the possibility of jointly reconstructing the modified GW propagation law and the linear bias of GW sources, as well as breaking the slight degeneracy between them by combining multiple techniques. We show predictions roughly based on a network of Einstein Telescopes combined with a high-redshift galaxy survey (z 3). Moreover, we investigate how these results can be re-scaled to other instrumental configurations. In the long run, we find that obtaining accurate and precise luminosity distance measurements (extracted directly from the individual GW signals) will be the most important factor to consider when maximizing constraining power.

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