Course
Chapters
- From data timelines to frequency maps
- Data model
- Imperfections and
various systematic effects: quality control
- Generic problem of
cartography, seen as a maximum likelihood problem
- Application to Planck
data: destriping methods
- Joint mapmaking and
cross-calibration problem
(*)
- Looking forward:
sub-pixel signal modelling, anisotropic beams, polarisation: what
remains to be done (*)
- Spectral estimation and cosmological
parameters (*)
- Traditional one channel
approach (cf JFC's lectures). Ideal likelihood
- Spectral estimation:
heuristic methods (small scales)
- Spectral estimation: the
case of polarised data, E/B coupling
- pectral estimation:
maximum likelihood methods, bayesian methods (large scales)
- Spectral estimation:
towards a hybrid likelihood ? (*)
- Cosmological inference:
MCMC and PMC. Link with the theory (*)
(*):
Briefly
Abstract
In these lectures, I will first try to brush a
broad picture of what constitutes a typical data analysis pipeline in
the context of a CMB experiment, with specific examples taken from the
Planck mission. The lectures will be naturally divided into two parts,
one concerning broadly the transition from timeline data to frequency
maps, and the second part describing more specifically the spectral
estimations methods developped so far. Finally, I will briefly describe
how these estimators can be used to do cosmological inference, using
the theoretical predictions described in the other lectures.
Bibliography
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fast iterative multi-grid map-making algorithm for CMB experiments
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- A maximum
likelihood approach to the destriping technique
Keihänen, E.; Kurki-Suonio, H.; Poutanen, T.;
Maino, D.; Burigana, C., Astronomy and Astrophysics, v.428,
p.287-298 (2004)
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- Destriping
CMB temperature and polarization maps
Kurki-Suonio, H.; Keihänen, E.; Keskitalo, R.;
Poutanen, T.;
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- Benchmark
parameters for CMB polarization experiments
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http://cdsads.u-strasbg.fr/abs/2003PhRvD..67d3004H
- Likelihood
techniques for the combined analysis of CMB temperature and
polarization power spectra
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- Estimating
the power spectrum of the cosmic microwave background
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Background Data Sets
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- Fast cosmic
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polarization with Gabor transforms
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- Fast
estimation of polarization power spectra using correlation functions
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Hivon, Eric;
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Society,
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- Cosmic
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and covariances
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Taylor, A. N., Monthly Notices of the
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- Myths and
truths concerning estimation of power spectra: the case for a hybrid
estimator
Efstathiou, G., Monthly Notices of the Royal Astronomical Society,
Volume 349, Issue 2, pp. 603-626.
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- Power
Spectrum Estimation from High-Resolution Maps by Gibbs Sampling
Eriksen, H. K.; O'Dwyer, I. J.;
Jewell, J. B.; Wandelt, B. D.;
Larson, D. L.; Górski, K. M.;
Levin, S.; Banday, A. J.; Lilje, P. B.,
The Astrophysical Journal Supplement Series, Volume 155, Issue 2, pp.
227-241
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- Fast optimal
CMB power spectrum estimation with Hamiltonian sampling
Taylor, J. F.; Ashdown, M. A. J.;
Hobson, M. P., Monthly Notices of the
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- TEASING: a
fast and accurate approximation for the low multipole likelihood of the
cosmic microwave background temperature
Benabed, K.; Cardoso, J.-F.; Prunet, S.; Hivon, E.,
Monthly Notices of
the Royal Astronomical Society, Volume 400, Issue 1, pp. 219-227.
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- Cosmological
parameters from CMB and other data: A Monte Carlo approach
Lewis, Antony; Bridle, Sarah, Physical Review D, vol. 66,
Issue 10, id. 103511.
http://cdsads.u-strasbg.fr/abs/2002PhRvD..66j3511L
- Estimation of
cosmological parameters using adaptive importance sampling
Wraith, Darren; Kilbinger, Martin; Benabed, Karim;
Cappé, Olivier;
Cardoso, Jean-François; Fort, Gersende;
Prunet, Simon;
Robert, Christian P., Physical Review D, vol. 80, Issue 2,
id. 023507
http://cdsads.u-strasbg.fr/abs/2009PhRvD..80b3507W
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