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LINNA: Likelihood Inference Neural Network Accelerator by To, Chun-Hao; Rozo, Eduardo; Krause, Elisabeth; Wu, Hao-Yi; Wechsler, Risa H.; Salcedo, Andrés N. is a scholarly article available to read on EtoBox.

What is LINNA: Likelihood Inference Neural Network Accelerator about?

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. Relative to the baseline DES analyses, LINNA reduces the computational cost associated with posterior inference by a factor of 8--50. If applied to the first-year cosmological analysis of Rubin Observatory's Legacy Survey of Space and Time (LSST Y1), we conservatively estimate that LINNA will save more than US $\$300,000$ on energy costs, while simultaneously reducing $\rm{CO}_2$ emission by $2,400$ tons. To accomplish these reductions, LINNA automatically builds training data sets, creates neural network surrogate models, and produces a Markov chain that samples the posterior. We explicitly verify that LINNA accurately reprod

Author

To, Chun-Hao; Rozo, Eduardo; Krause, Elisabeth; Wu, Hao-Yi; Wechsler, Risa H.; Salcedo, Andrés N.

Published

2022

Language

EN