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Cosmic shear: Inference from forward models
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC). Imperial College London, United Kingdom.
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Number of Authors: 62019 (English)In: Physical Review D: covering particles, fields, gravitation, and cosmology, ISSN 2470-0010, E-ISSN 2470-0029, Vol. 100, no 2, article id 023519Article in journal (Refereed) Published
Abstract [en]

Density-estimation likelihood-free inference (DELFI) has recently been proposed as an efficient method for simulation-based cosmological parameter inference. Compared to the standard likelihood-based Markov chain Monte Carlo (MCMC) approach, DELFI has several advantages: it is highly parallelizable, there is no need to assume a possibly incorrect functional form for the likelihood, and complicated effects (e.g., the mask and detector systematics) are easier to handle with forward models. In light of this, we present two DELFI pipelines to perform weak lensing parameter inference with log-normal realizations of the tomographic shear field-using the C-l summary statistic. The first pipeline accounts for the non-Gaussianities of the shear field, intrinsic alignments, and photometric-redshift error. We validate that it is accurate enough for Stage III experiments and estimate that O(1000) simulations are needed to perform inference on Stage IV data. By comparing the second DELFI pipeline, which makes no assumption about the functional form of the likelihood, with the standard MCMC approach, which assumes a Gaussian likelihood, we test the impact of the Gaussian likelihood approximation in the MCMC analysis. We find it has a negligible impact on Stage IV parameter constraints. Our pipeline is a step towards seamlessly propagating all data-processing, instrumental, theoretical, and astrophysical systematics through to the final parameter constraints.

Place, publisher, year, edition, pages
2019. Vol. 100, no 2, article id 023519
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Physical Sciences
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URN: urn:nbn:se:su:diva-171651DOI: 10.1103/PhysRevD.100.023519ISI: 000476694900004OAI: oai:DiVA.org:su-171651DiVA, id: diva2:1344705
Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-08-21Bibliographically approved

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Alsing, Justin
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