A New Strategy for Testing Cosmology with Simulations

Killedar, Madhura; Borgani, S.; Fabjan, D.; Dolag, K.; Granato, G. L.; Planelles, S.; Ragone-Figueroa, Cinthia

doi:10.1007/978-3-319-16238-6_10

Cited by 5 publications

(6 citation statements)

References 24 publications

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“…Lin & Kilbinger (2015b) and Lin et al (2016) showed that ABC is an efficient and successful parameter inference strategy for WL peak count analyses. The method has also been used recently in several other astrophysical and cosmological applications (Cameron & Pettitt 2012;Weyant et al 2013;Robin et al 2014;Killedar et al 2015;Ishida et al 2015;Akeret et al 2015). We constrain the parameter set (Ω m , σ 8 , w We implement a population Monte Carlo (PMC) ABC algorithm to iteratively converge on the posterior distribution of parameters.…”

Section: Parameter Inference With Approximate Bayesian Computationmentioning

confidence: 99%

Cosmological constraints with weak-lensing peak counts and second-order statistics in a large-field survey

Peel

Lin

Lanusse

et al. 2017

A&A

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Peak statistics in weak-lensing maps access the non-Gaussian information contained in the large-scale distribution of matter in the Universe. They are therefore a promising complementary probe to two-point and higher-order statistics to constrain our cosmological models. Next-generation galaxy surveys, with their advanced optics and large areas, will measure the cosmic weak-lensing signal with unprecedented precision. To prepare for these anticipated data sets, we assess the constraining power of peak counts in a simulated Euclid-like survey on the cosmological parameters Ω m , σ 8 , and w de 0 . In particular, we study how Camelus, a fast stochastic model for predicting peaks, can be applied to such large surveys. The algorithm avoids the need for time-costly N-body simulations, and its stochastic approach provides full PDF information of observables. Considering peaks with a signal-to-noise ratio ≥1, we measure the abundance histogram in a mock shear catalogue of approximately 5000 deg 2 using a multiscale mass-map filtering technique. We constrain the parameters of the mock survey using Camelus combined with approximate Bayesian computation, a robust likelihoodfree inference algorithm. Peak statistics yield a tight but significantly biased constraint in the σ 8 -Ω m plane, as measured by the width ∆Σ 8 of the 1σ contour. We find Σ 8 = σ 8 (Ω m /0.27) α = 0.77−0.05 with α = 0.75 for a flat ΛCDM model. The strong bias indicates the need to better understand and control the model systematics before applying it to a real survey of this size or larger. We perform a calibration of the model and compare results to those from the two-point correlation functions ξ ± measured on the same field. We calibrate the ξ ± result as well, since its contours are also biased, although not as severely as for peaks. In this case, we find for peaks Σ 8 = 0.76 −0.01 and α = 0.70. We conclude that the constraining power can therefore be comparable between the two weak-lensing observables in large-field surveys. Furthermore, the tilt in the σ 8 -Ω m degeneracy direction for peaks with respect to that of ξ ± suggests that a combined analysis would yield tighter constraints than either measure alone. As expected, w de 0 cannot be well constrained without a tomographic analysis, but its degeneracy directions with the other two varied parameters are still clear for both peaks and ξ ± .

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Section: Parameter Inference With Approximate Bayesian Computationmentioning

confidence: 99%

Cosmological constraints with weak-lensing peak counts and second-order statistics in a large-field survey

Peel

Lin

Lanusse

et al. 2017

A&A

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“…Plenty of variations, e.g. Markov Chain Monte Carlo ABC (Marjoram et al 2003), population Monte Carlo ABC (Beaumont et al 2009), lazy ABC (Prangle 2016), weighted ABC (Killedar et al 2015), or Hamiltonian ABC (Meeds et al 2015), have been proposed.…”

Section: State Of the Artmentioning

confidence: 99%

“…However, applications of ABC for astronomical purposes are still few. These are morphological transformation of galaxies (Cameron & Pettitt 2012), cosmological parameter inference using type Ia supernovae (Weyant et al 2013; (Killedar et al 2015), and the constraints on the halo occupation distribution (HOD) parameters (Hahn et al 2016). Apart from Paper II, three other software packages which allow to perform ABC in a general cosmological context have been released with their respective accompanied papers (Ishida et al 2015;Akeret et al 2015;Jennings & Madigan 2016).…”

Section: State Of the Artmentioning

confidence: 99%

Cosmology with weak-lensing peak counts

Lin

2016

Preprint

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English versionWeak gravitational lensing (WL) causes distortions of galaxy images and probes massive structures on large scales, allowing us to understand the late-time evolution of the Universe. One way to extract the cosmological information from WL is to use peak statistics. Peaks are tracers of massive halos and therefore probe the mass function. They retain non-Gaussian information and have already been shown as a promising tool to constrain cosmology. In this work, we develop a new model to predict WL peak counts. The model generates fast simulations based on halo sampling and selects peaks from the derived lensing maps. This approach has three main advantages. First, the model is very fast: only several seconds are required to perform a realization. Second, including realistic conditions is straightforward. Third, the model provides the full distribution information because of its stochasticity. We show that our model agrees well with N-body simulations. Then, we study the impacts of the cosmology-dependent covariance on constraints and explore different parameter inference methods. A special focus is put on approximate Bayesian computation (ABC), an acceptreject sampler without the need to estimate the likelihood. We show that ABC is able to yield robust constraints with much reduced time costs. Several filtering techniques are studied to improve the extraction of multiscale information. Finally, the new model is applied to the CFHTLenS, KiDS DR1/2, and DES SV data sets. Our preliminary results agree with the Planck constraints assuming the Lambda-CDM model. Overall, the thesis forges an innovative tool for future WL surveys. French versionL'effet de lentille gravitionnelle faible (WL) déforme les images des galaxies observées. Il porte des informations sur les grandes structures et nous apprend l'évolution de l'Univers. Une façon d'extraire des informations de WL est d'utiliser les statistiques d'ordres supérieurs à deux, en particulier le comptage de pics. Les pics indiquent la présence des halos et mesurent la fonction de masse. Ils sont considérés comme un bon outil pour contraindre la cosmologie. Dans cette thèse, on développe un nouveau modèle de prédiction sur le nombre des pics. Celui-ci génère des simulations des halos et sélectionne les pics à partir de la carte de WL résultante. Cette approche jouit de trois avantages. D'abord, le modèle est rapide: une réalisation s'obtient en quelques secondes. Ensuite, il est aisé d'y inclure les effets observationnels. Enfin, on a accès à la distribution complète des observables grâce à son caractère stochastique. On montre que notre modèle est en accord avec les simulations à N-corps. Puis, on examine des méthodes de contraintes variées. Un accent est mis sur la computation bayesienne approximative (ABC) qui est un processus d'acceptation/rejet ne nécessitant pas d'évaluer la fonction de vraisemblance. On montre que ABC fournit des contraintes cohérentes en un temps plus faible que la méthode classique. Des méthodes de filtrage, pour améliorer l'extractio...

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“…See Schafer and Freeman (2012); Akeret et al (2015); Ishida et al (2015) for reviews. Recent years have seen a rapid increase in the use of ABC methods for estimation in this field, including specific application to Milky way properties (Robin et al, 2014), strong lensing of galaxies (Killedar et al, 2018;Birrer, Amara and Refregier, 2017), large scale structure of the Universe , estimating the redshift distribution (Herbel et al, 2017), galaxy evolution (Hahn, Tinker and Wetzel, 2017), weak lensing (Peel et al, 2017;Lin and Kilbinger, 2015), exoplanets (Parker, 2015), galaxy morphology (Cameron and Pettitt, 2012), and supernovae (Weyant, Schafer and Wood-Vasey, 2013).…”

Section: Introductionmentioning

confidence: 99%

A preferential attachment model for the stellar initial mass function

Cisewski-Kehe

Weller²,

Schafer

2019

Electron. J. Statist.

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Accurate specification of a likelihood function is becoming increasingly difficult in many inference problems in astronomy. As sample sizes resulting from astronomical surveys continue to grow, deficiencies in the likelihood function lead to larger biases in key parameter estimates. These deficiencies result from the oversimplification of the physical processes that generated the data, and from the failure to account for observational limitations. Unfortunately, realistic models often do not yield an analytical form for the likelihood. The estimation of a stellar initial mass function (IMF) is an important example. The stellar IMF is the mass distribution of stars initially formed in a given cluster of stars, a population which is not directly observable due to stellar evolution and other disruptions and observational limitations of the cluster. There are several difficulties with specifying a likelihood in this setting since the physical processes and observational challenges result in measurable masses that cannot legitimately be considered independent draws from an IMF. This work improves inference of the IMF by using an approximate Bayesian computation approach that both accounts for observational and astrophysical effects and incorporates a physically-motivated model for star cluster formation. The methodology is illustrated via a simulation study, demonstrating that the proposed approach can recover the true posterior in realistic situations, and applied to observations from astrophysical simulation data.

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A New Strategy for Testing Cosmology with Simulations

Cited by 5 publications

References 24 publications

Cosmological constraints with weak-lensing peak counts and second-order statistics in a large-field survey

Cosmological constraints with weak-lensing peak counts and second-order statistics in a large-field survey

Cosmology with weak-lensing peak counts

A preferential attachment model for the stellar initial mass function

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