S
Simon Prunet
Researcher at Institut d'Astrophysique de Paris
Publications - 439
Citations - 102156
Simon Prunet is an academic researcher from Institut d'Astrophysique de Paris. The author has contributed to research in topics: Cosmic microwave background & Planck. The author has an hindex of 141, co-authored 434 publications receiving 96314 citations. Previous affiliations of Simon Prunet include University of Hawaii & University of Toronto.
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Proceedings ArticleDOI
Simulating weak lensing on CMB maps
TL;DR: In this paper, a fast, arbitrarily accurate method to simulate the effect of gravitational lensing of the CMB anisotropies and polarization fields by largescale structures on arbitrarily spaced grid points over a unit sphere using Non-equaspaced fast Fourier transform (NFFT) is presented.
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Fast quadratic power spectrum estimators and the E–B decomposition
TL;DR: In this article, a fast, quadratic power spectrum estimator for cosmic microwave background polarization fields, based on heuristically-weighted correlation functions, is described. But the method can handle real-world effects such as inhomogeneous or correlated noise, and arbitrary sky cuts.
Journal ArticleDOI
Comparison of map-making algorithms for CMB experiments
T. Poutanen,T. Poutanen,G. de Gasperis,Eric Hivon,Hannu Kurki-Suonio,Amedeo Balbi,Julian Borrill,Julian Borrill,C. Cantalupo,C. Cantalupo,Olivier Doré,E. Keihänen,E. Keihänen,Charles R. Lawrence,Davide Maino,Paolo Natoli,Simon Prunet,Radek Stompor,Radek Stompor,Romain Teyssier +19 more
TL;DR: In this article, the authors compared the results of the maximum-likelihood (ML) map-making and the destriping algorithm for the cosmic microwave background (CMB) temperature anisotropy maps made from one-year time ordered data (TOD) streams.
Proceedings ArticleDOI
The Skeleton: Connecting Large Scale Structures to Galaxy Formation
Christophe Pichon,Dmitry Pogosyan,Simon Prunet,Thierry Sousbie,Stephane Colombi,Adrianne Slyz,Julien Devriendt +6 more
TL;DR: In this paper, morphological estimators of the filamentary structure of the cosmic web are presented. But the authors focus on the global and local skeletons of the Cosmic Web and do not consider the local skeletons.
Journal ArticleDOI
A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: II. HII Region LineRatios
Carter Rhea,Laurie Rousseau-Nepton,Simon Prunet,M. Prasow-Émond,Julie Hlavacek-Larrondo,N. V. Asari,Kathryn Grasha,Laurence Perreault-Levasseur +7 more
TL;DR: In this paper, an artificial neural network was proposed to estimate the line ratios of strong emission-lines present in the SN1, SN2, and SN3 filters of the Canada-France-Hawaii Telescope.