LINNA: Likelihood Inference Neural Network Accelerator

Understand

Bayesian posterior inference of modern multi-probe cosmological analyses incurs massive computational costs.

• For instance, depending on the combinations of probes, a single posterior inference for the Dark Energy Survey (DES) data had a wall-clock time that ranged from 1 to 21 days using a state-of-the-art computing cluster with 100 cores.
• These computational costs have severe environmental impacts and the long wall-clock time slows scientific productivity.
• To address these difficulties, we introduce LINNA: the Likelihood Inference Neural Network Accelerator.