Compressed 'CMB-lite' Likelihoods Using Automatic Differentiation

Understand

The compression of multi-frequency cosmic microwave background (CMB) power spectrum measurements into a series of foreground-marginalised CMB-only band powers allows for the construction of faster and more easily interpretable 'lite' likelihoods.

• However, obtaining the compressed data vector is computationally expensive and yields a covariance matrix with sampling noise.
• In this work, we present an implementation of the CMB-lite framework relying on automatic differentiation.
• The technique presented reduces the computational cost of the lite likelihood construction to one minimisation and one Hessian evaluation, which run on a personal computer in about a minute.