Weak-lensing peaks in simulated light cones: investigating the coupling between dark matter and dark energy

Giocoli, Carlo; Moscardini, L.; Baldi, Marco; Meneghetti, M.; Metcalf, R. Benton

doi:10.1093/mnras/sty1312

Cited by 21 publications

(20 citation statements)

References 91 publications

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“…The DUSTGRAIN-pathfinder simulations have been devised to sample the { f R0 , m ν } parameter space and to identify highly degenerate combinations of parameters. Some analyses of the corresponding WL signal have been presented by Giocoli et al (2018a) and Peel et al (2018), while Hagstotz et al (2018) have used the simulations to calibrate a theoretical modelling of the halo mass function in f (R) gravity with and without the contribution of massive neutrinos. In this further paper, we will use machine learning techniques to tackle the issue of observational degeneracy in these combined models based on the WL reconstruction described in Giocoli et al (2018a).…”

Section: Dustgrain-pathfindermentioning

confidence: 99%

On the dissection of degenerate cosmologies with machine learning

Merten¹,

Giocoli

Baldi

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos. Three types of machine learning techniques are tested for their ability to discriminate lensing convergence maps by extracting dimensional reduced representations of the data. Classical map descriptors such as the power spectrum, peak counts and Minkowski functionals are combined into a joint feature vector and compared to the descriptors and statistics that are common to the field of digital image processing. To learn new features directly from the data we use a Convolutional Neural Network (CNN). For the mapping between feature vectors and the predictions of their underlying model, we implement two different classifiers; one based on a nearest-neighbour search and one that is based on a fully connected neural network. We find that the neural network provides a much more robust classification than the nearest-neighbour approach and that the CNN provides the most discriminating representation of the data. It achieves the cleanest separation between the different models and the highest classification success rate of 59% for a single source redshift. Once we perform a tomographic CNN analysis, the total classification accuracy increases significantly to 76% with no observational degeneracies remaining. Visualising the filter responses of the CNN at different network depths provides us with the unique opportunity to learn from very complex models and to understand better why they perform so well.

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Section: Dustgrain-pathfindermentioning

confidence: 99%

On the dissection of degenerate cosmologies with machine learning

Merten¹,

Giocoli

Baldi

et al. 2019

Monthly Notices of the Royal Astronomical Society

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“…Two-point statistics fail to capture this non-Gaussian information and thus yield an incomplete description of the matter distribution at low redshift. To close this gap, the community has recently started to explore non-Gaussian cosmic shear estimators: for example weak-lensing peaks (e.g., Kruse & Schneider 1999, 2000Dietrich & Hartlap 2010;Kratochvil et al 2010;Fan et al 2010;Yang et al 2011;Maturi et al 2011;Hamana et al 2012;Hilbert et al 2012;Marian et al 2012Marian et al , 2013Shan et al 2014Shan et al , 2018Lin & Kilbinger 2015;Martinet et al 2015Martinet et al , 2018Liu et al 2015a,b;Kacprzak et al 2016;Petri et al 2016;Zorrilla Matilla et al 2016;Giocoli et al 2018;Peel et al 2018;Davies et al 2019;Fong et al 2019;Li et al 2019;Weiss et al 2019;Yuan et al 2019;Coulton et al 2020;Ajani et al 2020;Zürcher et al 2021), Minkowski functionals (e.g., Kratochvil et al 2012;Petri et al 2015;Vicinanza et al 2019;Parroni et al 2020;Zürcher et al 2021), higher-order moments (e.g., Van Waerbeke et al 2013;Petri et al 2015;Peel et al 2018;Vicinanza et al 2018;…”

Section: Introductionmentioning

confidence: 99%

Probing dark energy with tomographic weak-lensing aperture mass statistics

Martinet

Harnois-Déraps

Jullo

et al. 2021

A&A

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We forecast and optimize the cosmological power of various weak-lensing aperture mass (Map) map statistics for future cosmic shear surveys, including peaks, voids, and the full distribution of pixels (1D Map). These alternative methods probe the non-Gaussian regime of the matter distribution, adding complementary cosmological information to the classical two-point estimators. Based on the SLICS and cosmo-SLICS N-body simulations, we build Euclid-like mocks to explore the S8 − Ωm − w0 parameter space. We develop a new tomographic formalism that exploits the cross-information between redshift slices (cross-Map) in addition to the information from individual slices (auto-Map) probed in the standard approach. Our auto-Map forecast precision is in good agreement with the recent literature on weak-lensing peak statistics and is improved by ∼50% when including cross-Map. It is further boosted by the use of 1D Map that outperforms all other estimators, including the shear two-point correlation function (γ-2PCF). When considering all tomographic terms, our uncertainty range on the structure growth parameter S8 is enhanced by ∼45% (almost twice better) when combining 1D Map and the γ-2PCF compared to the γ-2PCF alone. We additionally measure the first combined forecasts on the dark energy equation of state w0, finding a factor of three reduction in the statistical error compared to the γ-2PCF alone. This demonstrates that the complementary cosmological information explored by non-Gaussian Map map statistics not only offers the potential to improve the constraints on the recent σ8–Ωm tension, but also constitutes an avenue to understanding the accelerated expansion of our Universe.

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“…Nevertheless, these nonGaussian estimators are difficult to predict theoretically because of limits in our understanding of the nonlinear growth of structures (see e.g., Fan et al 2010;Lin & Kilbinger 2015;Shan et al 2018;Giocoli et al 2018a; Barthelemy et al 2021, for some attempts) and are instead modeled with N-body simulations. This can significantly increase the computational cost of such analyses, but resorting to N-body simulations is also necessary to accurately model the γ-2PCF at scales affected by nonlinearities (e.g., Euclid Collaboration 2020).…”

Section: Introductionmentioning

confidence: 99%

Impact of baryons in cosmic shear analyses with tomographic aperture mass statistics

Martinet

Castro

Harnois-Déraps

et al. 2021

A&A

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NonGaussian cosmic shear statistics based on weak-lensing aperture mass (Map) maps can outperform the classical shear two-point correlation function (γ-2PCF) in terms of cosmological constraining power. However, reaching the full potential of these new estimators requires accurate modeling of the physics of baryons as the extra nonGaussian information mostly resides at small scales. We present one such modeling based on the Magneticum hydrodynamical simulation for the KiDS-450 and DES-Y1 surveys and a Euclid-like survey. We compute the bias due to baryons on the lensing PDF and the distribution of peaks and voids in Map maps and propagate it to the cosmological forecasts on the structure growth parameter S8, the matter density parameter Ωm, and the dark energy equation of state w0 using the SLICS and cosmo-SLICS sets of dark-matter-only simulations. We report a negative bias of a few percent on S8 and Ωm and also measure a positive bias of the same level on w0 when including a tomographic decomposition. These biases reach ∼5% when combining Map statistics with the γ-2PCF as these estimators show similar dependency on the AGN feedback. We verify that these biases constitute a less than 1σ shift on the probed cosmological parameters for current cosmic shear surveys. However, baryons need to be accounted for at the percentage level for future Stage IV surveys and we propose to include the uncertainty on the AGN feedback amplitude by marginalizing over this parameter using multiple simulations such as those presented in this paper. Finally, we explore the possibility of mitigating the impact of baryons by filtering the Map map but find that this process would require suppressing the small-scale information to a point where the constraints would no longer be competitive.

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Weak-lensing peaks in simulated light cones: investigating the coupling between dark matter and dark energy

Cited by 21 publications

References 91 publications

On the dissection of degenerate cosmologies with machine learning

On the dissection of degenerate cosmologies with machine learning

Probing dark energy with tomographic weak-lensing aperture mass statistics

Impact of baryons in cosmic shear analyses with tomographic aperture mass statistics

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