November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

Assessing and Benchmarking the Fidelity of Posterior Inference Methods for Astrophysics Data Analysis

Nov 30, 2023, 5:30 PM
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Flash talk Online Contributed talks


Becky Nevin (Fermilab)


In this era of large and complex astronomical survey data, interpreting, validating, and comparing inference techniques becomes increasingly difficult. This is particularly critical for emerging inference methods like Simulation-Based Inference (SBI), which offer significant speedup potential and posterior modeling flexibility, especially when deep learning is incorporated. We present a study to assess and compare the performance and uncertainty prediction capability of Bayesian inference algorithms – from traditional MCMC sampling of analytic functions to deep learning-enabled SBI. We focus on testing the capacity of hierarchical inference modeling in those scenarios. Before we extend this study to cosmology, we first use astrophysical simulation data to ensure interpretability. We demonstrate a probabilistic programming implementation of hierarchical and non-hierarchical Bayesian inference using simulations derived from the DeepBench software library, a benchmarking tool developed by our group that generates simple and controllable astrophysical objects from first principles. This study will enable astronomers and physicists to harness the inference potential of these methods with confidence.

Primary author

Becky Nevin (Fermilab)


Aleksandra Ciprijanovic (Fermi National Accelerator Laboratory) Brian Nord (Fermilab) Jason Poh (University of Chicago) Samuel McDermott (University of Chicago) Sreevani Jarugula (Fermilab)

Presentation materials