Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys
| Autor: | Natalia Porqueres, Guilhem Lavaux, Jens Jasche, Doogesh Kodi Ramanah |
|---|---|
| Přispěvatelé: | Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut Lagrange de Paris, Sorbonne Université (SU), ANR-16-CE23-0002,BIG4,Grosses données, Grosses simulations, Big Bang et Grands problèmes: Algorithes de reconstruction bayésiennes contraintes par la physique et application à l'analyse de données cosmologiques(2016), Sorbonne Universités |
| Rok vydání: | 2019 |
| Předmět: |
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
Dark matter Bayesian probability FOS: Physical sciences Astrophysics Astrophysics::Cosmology and Extragalactic Astrophysics 01 natural sciences Cosmology 0103 physical sciences Range (statistics) [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det] Cluster analysis 010303 astronomy & astrophysics Instrumentation and Methods for Astrophysics (astro-ph.IM) galaxies: statistics Physics methods: statistical 010308 nuclear & particles physics Matter power spectrum Spectral density Astronomy and Astrophysics methods: data analysis ddc Data set Space and Planetary Science cosmology: observations large-scale structure of Universe Astrophysics - Instrumentation and Methods for Astrophysics [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] Algorithm Astrophysics - Cosmology and Nongalactic Astrophysics |
| Zdroj: | Astronomy and Astrophysics-A&A Astronomy and Astrophysics-A&A, EDP Sciences, 2019, 624, pp.A115. ⟨10.1051/0004-6361/201834844⟩ Astronomy and Astrophysics-A&A, 2019, 624, pp.A115. ⟨10.1051/0004-6361/201834844⟩ Astron.Astrophys. Astron.Astrophys., 2019, 624, pp.A115. ⟨10.1051/0004-6361/201834844⟩ |
| ISSN: | 0004-6361 |
| DOI: | 10.1051/0004-6361/201834844⟩ |
| Popis: | International audience; The treatment of unknown foreground contaminations will be one of the major challenges for galaxy clustering analyses of coming decadal surveys. These data contaminations introduce erroneous large-scale effects in recovered power spectra and inferred dark matter density fields. In this work, we present an effective solution to this problem in the form of a robust likelihood designed to account for effects due to unknown foreground and target contaminations. Conceptually, this robust likelihood marginalizes over the unknown large-scale contamination amplitudes. We showcase the effectiveness of this novel likelihood via an application to a mock SDSS-III data set subject to dust extinction contamination. In order to illustrate the performance of our proposed likelihood, we infer the underlying dark-matter density field and reconstruct the matter power spectrum, being maximally agnostic about the foregrounds. The results are compared to those of an analysis with a standard Poissonian likelihood, as typically used in modern large-scale structure analyses. While the standard Poissonian analysis yields excessive power for large-scale modes and introduces an overall bias in the power spectrum, our likelihood provides unbiased estimates of the matter power spectrum over the entire range of Fourier modes considered in this work. Further, we demonstrate that our approach accurately accounts for and corrects the effects of unknown foreground contaminations when inferring three-dimensional density fields. Robust likelihood approaches, as presented in this work, will be crucial to control unknown systematic error and maximize the outcome of the decadal surveys.Key words: methods: data analysis / methods: statistical / galaxies: statistics / cosmology: observations / large-scale structure of Universe |
| Databáze: | OpenAIRE |
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