<i>Euclid</i> preparation: II. The <scp>EuclidEmulator</scp> – a tool to compute the cosmology dependence of the nonlinear matter power spectrum

Knabenhans, Mischa; Stadel, Joachim; Marelli, Stefano; Potter, D.; Teyssier, Romain; Legrand, Laurent; Schneider, Aurel; Sudret, Bruno; Blot, Linda; Awan, S.; Burigana, C.; Carvalho, C. S.; Kurki-Suonio, H.; Sirri, G.

doi:10.1093/mnras/stz197

Cited by 151 publications

(125 citation statements)

References 63 publications

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“…To do this, it is important to further improve the efficiency of the inference pipeline and to generate more N-body simulations. Recent work on cosmological emulators [85] and the release of new N-body simulations [86] suggests that the simulation-driven inference of increasing number of cosmological parameters is becoming feasible. Importantly, the simulation-level implementations of realistic baryon feedback models will most likely prove to be crucial for future analyses with deep learning.…”

Section: Discussionmentioning

confidence: 99%

Cosmological constraints with deep learning from KiDS-450 weak lensing maps

et al. 2019

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Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density Ωm, the fluctuation amplitude σ8, and the intrinsic alignment amplitude AIA. We use a grid of N-body simulations to generate a training set of tomographic weak lensing maps. We test the robustness of the expected constraints to various effects, such as baryonic feedback, simulation accuracy, different value of H0, or the lightcone projection technique. We train a set of ResNet-based CNNs with varying depths to analyze sets of tomographic KiDS mass maps divided into 20 flat regions, with applied Gaussian smoothing of σ = 2.34 arcmin. The uncertainties on shear calibration and n(z) error are marginalized in the likelihood pipeline. Following a blinding scheme, we derive constraints of S8 = σ8(Ωm/0.3) 0.5 = 0.777 +0.038 −0.036 with our CNN analysis, with AIA = 1.398 +0.779 −0.724 . We compare this result to the power spectrum analysis on the same maps and likelihood pipeline and find an improvement of about 30% for the CNN. We discuss how our results offer excellent prospects for the use of deep learning in future cosmological data analysis.

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Section: Discussionmentioning

confidence: 99%

Cosmological constraints with deep learning from KiDS-450 weak lensing maps

et al. 2019

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“…The advantage of this scheme is that it does not require additional samples for additional dimensions. LHS techniques have been used to considerable success in other high-dimensional astrophysical applications (Knabenhans et al 2019).…”

Section: Sampling Strategymentioning

confidence: 99%

Machine learning applied to simulations of collisions between rotating, differentiated planets

Timpe

Veiga

Knabenhans

et al. 2020

Comput. Astrophys. Cosmol.

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In the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of classifying and predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and classification and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations, while avoiding the cost of direct simulation. This work is based on a new set of 14,856 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.

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“…Our µ precisely parametrises the correction necessary to obtain the potential from the matter power spectrum, and additionally allows to reconstruct the power spectrum of k-essence perturbations. An immediate practical application would be to include this correction when calibrating emulators like [15].…”

Section: Improving Newtonian Simulationsmentioning

confidence: 99%

Parametrising non-linear dark energy perturbations

Hassani

L’Huillier

Shafieloo

et al. 2020

J. Cosmol. Astropart. Phys.

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In this paper we quantify the non-linear effects from k-essence dark energy through an effective parameter µ that encodes the additional contribution of a dark energy fluid or a modification of gravity to the Poisson equation. This is a first step toward quantifying non-linear effects of dark energy/modified gravity models in a more general approach. We compare our simulation results with predictions from the linear Boltzmann code CLASS. We show that for a large k-essence speed of sound the CLASS results are sufficiently accurate, while for a low speed of sound non-linearities in matter and in the k-essence field are non-negligible. We propose a tanh-based parameterisation for µ to include the non-linear effects based on the simulation results and this parametric form of µ can be used to improve Fisher forecasts or Newtonian N -body simulations for k-essence models.

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Euclid preparation: II. The EuclidEmulator – a tool to compute the cosmology dependence of the nonlinear matter power spectrum

Cited by 151 publications

References 63 publications

Cosmological constraints with deep learning from KiDS-450 weak lensing maps

Cosmological constraints with deep learning from KiDS-450 weak lensing maps

Machine learning applied to simulations of collisions between rotating, differentiated planets

Parametrising non-linear dark energy perturbations

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