Time-domain Gibbs sampling
About the project
The detection of primordial gravity waves created during the Big Bang ranks among the greatest potential intellectual achievements in modern science. During the last few decades, the instrumental progress necessary to achieve this has been nothing short of breathtaking, and we are today able to measure the microwave sky with better than one-in-a-million precision. However, from the latest ultra-sensitive experiments such as BICEP2 and Planck, it is clear that instrumental sensitivity alone will not be sufficient to make a robust detection of gravitational waves. Contamination in the form of astrophysical radiation from the Milky Way, for instance thermal dust and synchrotron radiation, obscures the cosmological signal by orders of magnitude. Even more critically, though, are second-order interactions between this radiation and the instrument characterization itself that lead to a highly non-linear and complicated problem.
Recognizing the lessons learned from Planck, the defining design philosophy of Commander3 is tight integration of all steps from raw time-ordered data processing to high-level cosmological parameter estimation. Traditionally, this process has been carried out in a series of weakly connected steps, pipelining independent executables with or without human intervention. Some steps have mostly relied on frequentist statistics, employing forward simulations to propagate uncertainties, while other steps have adopted a Bayesian approach. For instance, traditional mapmaking is a typical example of the former, while cosmological parameter estimation is a typical example of the latter; for component separation purposes, both approaches have been explored in the literature.
Commander3 is the first real-world CMB analysis pipeline to adopt an end-to-end Bayesian approach. First proposed in 2005, it took more than 15 years of computational and
algorithmic developments to actually make it feasible.
Perhaps the single most important advantage of a uniform Bayesian approach is that it allows seamless propagation of uncertainties within a well-established statistical framework, from raw time-ordered data to cosmological parameters. This aspect will become critically
important for future experiments, as demonstrated by Planck. For most CMB experiments prior to Planck, the dominant source of uncertainty was noise; for most CMB experiments after Planck, the dominant source of uncertainty will be instrumental systematics, foreground contamination, and the interplay between the two. As a logical consequence of this fact, Commadner3 adopts a consistent statistical framework that integrates detailed error propagation as a foundational feature.
This project is financed by Horizon2020 through two grants:
- ERC consolidator grant of €2M (2018-2022) for the BeyondPlanck collaboration
- SPACE COMPET-4 network grant of €1.5M (2018-2020)
Commander3 is developed by the BeyondPlanck collaboration led by University of Oslo with the following international partners:
- University of Milano
- INAF Trieste
- University of Helsinki
- Planetek Greece.