On the computational feasibility of Bayesian end-to-end analysis of LiteBIRD simulations within Cosmoglobe

TL;DR

This paper evaluates the computational feasibility of performing full Bayesian analysis of LiteBIRD satellite data, estimating data volumes and processing times, and concludes that future high-performance computing can handle the workload.

Contribution

It provides a detailed assessment of the computational resources needed for end-to-end Bayesian analysis of LiteBIRD simulations, demonstrating feasibility with future HPC systems.

Findings

Estimated data volume for full mission: 238 TB

Approximate time for one Gibbs sample: 3000 CPU hours

Current idealized simulations suggest feasibility with future HPC

Abstract

We assess the computational feasibility of end-to-end Bayesian analysis of the JAXA-led LiteBIRD experiment by analysing simulated time ordered data (TOD) for a subset of detectors through the Cosmoglobe and Commander3 framework. The data volume for the simulated TOD is 1.55 TB, or 470 GB after Huffman compression. From this we estimate a total data volume of 238 TB for the full three year mission, or 70 TB after Huffman compression. We further estimate the running time for one Gibbs sample, from TOD to cosmological parameters, to be approximately 3000 CPU hours. The current simulations are based on an ideal instrument model, only including correlated 1/f noise. Future work will consider realistic systematics with full end-to-end error propagation. We conclude that these requirements are well within capabilities of future high-performance computing systems.