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2019

Data compression in cosmology: A compressed likelihood for Planck data

Prince, Heather, Dunkley, Jo

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We apply the massively optimized parameter estimation and data compression technique (MOPED) to the public Planck 2015 temperature likelihood, reducing the dimensions of the data space to one number per parameter of interest.

We present CosMOPED, a lightweight and convenient compressed likelihood code implemented in Python.
In doing so we show that the $\ell<30$ Planck temperature likelihood can be well approximated by two Gaussian distributed data points, which allows us to replace the map-based low-$\ell$ temperature likelihood by a simple Gaussian likelihood.
We make available a Python implementation of Planck's 2015 Plik_lite temperature likelihood that includes these low-$\ell$ binned temperature data (Planck-lite-py).

Data compression in cosmology: A compressed likelihood for Planck data · Around

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Planck Collaboration, Planck 2018 results. V. CMB power spectra and likelihoods, arXiv e-prints , arXiv:1907.12875 (2019), arXiv:1907.12875 [astro-ph.CO]
Original
1907
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C. Reichardt, R. Jimenez, and A. F. Heavens, Recovering physical parameters from galaxy spectra using MOPED, Monthly Notices of the Royal Astronomical Society 327
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A. Heavens, B. Panter, R. Jimenez, and J. Dunlop, The star-formation history of the Universe from the stellar populations of nearby galaxies, Nature (London) 428
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