Optimal machine-driven acquisition of future cosmological data

Kostić, Andrija; Jasche, Jens; Ramanah, Doogesh Kodi; Lavaux, Guilhem

doi:10.1051/0004-6361/202141706

Cited by 6 publications

(3 citation statements)

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“…Adding velocity information to the Bayesian reconstruction methods can potentially lead to impro v ed reconstruction of the full phase space structure (density + velocity) of dark matter (Leclercq et al 2017 ). Such phase space reconstruction can then potentially guide observ ation ef forts for ne w disco v eries (Kosti ć et al 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Bayesian reconstruction of dark matter distribution from peculiar velocities: accounting for inhomogeneous Malmquist bias

Boruah

Lavaux

Hudson

2022

Monthly Notices of the Royal Astronomical Society

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We present a Bayesian velocity field reconstruction algorithm that performs the reconstruction of the mass density field using only peculiar velocity data. Our method consistently accounts for the inhomogeneous Malmquist bias using analytic integration along the line-of-sight. By testing our method on a simulation, we show that our method gives an unbiased reconstruction of the velocity field. We show that not accounting for the inhomogeneous Malmquist bias can lead to significant biases in the Bayesian reconstructions. We applied our method to a peculiar velocity data set consisting of the SFI++ and 2MTF Tully-Fisher catalogues and the A2 supernovae compilation, thus obtaining a novel velocity reconstruction in the local Universe. Our velocity reconstructions have a cosmological power spectrum consistent with the theoretical expectation. Furthermore, we obtain a full description of the uncertainties on reconstruction through samples of the posterior distribution. We validate our velocity reconstruction of the local Universe by comparing it to an independent reconstruction using the 2M++ galaxy catalogue, obtaining good agreement between the two reconstructions. Using Bayesian model comparison, we find that our velocity model performs better than the adaptive kernel smoothed velocity with the same peculiar velocity data. However, our velocity model does not perform as well as the velocity reconstruction from the 2M++ galaxy catalogue, due to the sparse and noisy nature of the peculiar velocity tracer samples. The method presented here provides a way to include peculiar velocity data in initial condition reconstruction frameworks.

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Section: Discussionmentioning

confidence: 99%

Bayesian reconstruction of dark matter distribution from peculiar velocities: accounting for inhomogeneous Malmquist bias

Boruah

Lavaux

Hudson

2022

Monthly Notices of the Royal Astronomical Society

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“…In principle, these maps could be used by observational missions to further probe the signals of PNG, e.g., searching for small-scale effects of 𝑓 nl or studying the properties of galaxies in these regions. In other words, our method can highlight regions where PNG is expected to be imprinted given the data (Kostić et al 2022).…”

Section: Accessing the Field Of Primordial Matter Fluctuationsmentioning

confidence: 99%

Bayesian field-level inference of primordial non-Gaussianity using next-generation galaxy surveys

Andrews¹,

Jasche²,

Lavaux³

2022

Preprint

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Detecting and measuring a non-Gaussian signature of primordial origin in the density field is a major science goal of nextgeneration galaxy surveys. The signal will permit us to determine primordial physics processes and constrain models of cosmic inflation. While traditional approaches utilise a limited set of statistical summaries of the galaxy distribution to constrain primordial non-Gaussianity, we present a field-level approach by Bayesian forward-modelling the entire three-dimensional galaxy survey. Our method naturally and fully self-consistently exploits the entirety of the large-scale structure, e.g., higherorder statistics, peculiar velocity fields, and scale-dependent galaxy bias, to extract information on the local non-Gaussianity parameter, 𝑓 nl . We demonstrate the performance of our approach through various tests with mock galaxy data emulating relevant features of the SDSS-III/BOSS-like survey, and additional tests with a Stage IV mock data set. These tests reveal that the method infers unbiased values of 𝑓 nl by accurately handling survey geometries, noise, and unknown galaxy biases. We demonstrate that our method can achieve constraints of 𝜎 𝑓 nl ≈ 8.78 for SDSS-III/BOSS-like data, an improvement of a factor ∼ 2.5 over currently published constraints. Tests with next-generation mock data show that significant further improvements are feasible with sufficiently high resolution. Furthermore, the results demonstrate that our method can consistently marginalise all nuisance parameters of the data model. The method further provides an inference of the three-dimensional primordial density field, providing opportunities to explore additional signatures of primordial physics.

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“…These have become increasingly popular as the amount of information extracted from cosmological surveys has become overwhelming. Machine learning techniques have already been used in a variety of cosmology setups: CMB [8,9], LSS [10][11][12][13], reionization and 21cm [14,15], gravitational lensing: weak lensing [16,17], strong lensing [18,19], redshift prediction [20,21], parameter estimation [22,23], and are expected to provide more insights in the future [24]. In particular, such techniques can be applied to observations of the largescale structure measured from galaxy surveys.…”

Section: Introductionmentioning

confidence: 99%

Extracting cosmological parameters from N-body simulations using machine learning techniques

Lazanu

2021

J. Cosmol. Astropart. Phys.

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We make use of snapshots taken from the Quijote suite of simulations, consisting of 2000 simulations where five cosmological parameters have been varied (Ωm, Ωb, h, n s and σ8) in order to investigate the possibility of determining them using machine learning techniques. In particular, we show that convolutional neural networks can be employed to accurately extract Ω m and σ 8 from the N-body simulations, and that these parameters can also be found from the non-linear matter power spectrum obtained from the same suite of simulations using both random forest regressors and deep neural networks. We show that the power spectrum provides competitive results in terms of accuracy compared to using the simulations and that we can also estimate the scalar spectral index n s from the power spectrum, at a lower precision.

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Optimal machine-driven acquisition of future cosmological data

Cited by 6 publications

References 50 publications

Bayesian reconstruction of dark matter distribution from peculiar velocities: accounting for inhomogeneous Malmquist bias

Bayesian reconstruction of dark matter distribution from peculiar velocities: accounting for inhomogeneous Malmquist bias

Bayesian field-level inference of primordial non-Gaussianity using next-generation galaxy surveys

Extracting cosmological parameters from N-body simulations using machine learning techniques

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