COSMOGLOBE: Towards end-to-end CMB cosmological parameter estimation without likelihood approximations

¹ Institute of Theoretical Astrophysics, University of Oslo, Blindern, Oslo, Norway
e-mail: j.r.eskilt@astro.uio.no
² Imperial Centre for Inference and Cosmology, Department of Physics, Imperial College London, Blackett Laboratory, Prince Consort Road, London SW7 2AZ, UK
³ Department of Physics, Indian Institute of Technology (BHU), Varanasi 221005, India
⁴ Dipartimento di Fisica, Università degli Studi di Milano, Via Celoria, 16, Milano, Italy
⁵ Instituto de Física, Universidade de São Paulo, 66318, CEP 05315-970, São Paulo, Brazil
⁶ Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T1Z1, Canada
⁷ David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada
⁸ Dunlap Institute for Astronomy & Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada
⁹ Waterloo Centre for Astrophysics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
¹⁰ Department of Physics and Astronomy, University of Waterloo, Waterloo, ON N2L 3G1, Canada
¹¹ Indian Institute of Astrophysics, Koramangala II Block, Bangalore 560034, India

Received: 4 July 2023
Accepted: 24 August 2023

Abstract

We implement support for a cosmological parameter estimation algorithm in Commander and quantify its computational efficiency and cost. For a semi-realistic simulation similar to Planck LFI 70 GHz, we find that the computational cost of producing one single sample is about 20 CPU-hours and that the typical Markov chain correlation length is ∼100 samples. The net effective cost per independent sample is ∼2000 CPU-hours, in comparison with all low-level processing costs of 812 CPU-hours for Planck LFI and WMAP in COSMOGLOBE Data Release 1. Thus, although technically possible to run already in its current state, future work should aim to reduce the effective cost per independent sample by one order of magnitude to avoid excessive runtimes, for instance through multi-grid preconditioners and/or derivative-based Markov chain sampling schemes. This work demonstrates the computational feasibility of true Bayesian cosmological parameter estimation with end-to-end error propagation for high-precision CMB experiments without likelihood approximations, but it also highlights the need for additional optimizations before it is ready for full production-level analysis.