Title: Quality over Quantity in Simulation-Based Inference: Inference of Dark Matter Physics using Strong Gravitational Lenses
Abstract: Strong gravitational lenses are a singular probe of the Universe's small-scale structure --- they are sensitive to the gravitational effects of low-mass (10^10 M_sun) halos even without a luminous counterpart. Recent strong-lensing analyses of dark matter structure rely on simulation-based inference (SBI). Modern SBI methods, which leverage neural networks as density estimators, have shown promise in extracting the halo-population signal. However, it is unclear whether the constraining power of these models has been limited by the methodology or the information content of the data. In this talk, we will show that the performance of the fiducial SBI analysis is fully dictated by the training set size. We then adopt a sequential neural posterior estimation (SNPE) approach, allowing us to iteratively refine the distribution of simulated training images to better align with the observed data. Using only one-fifth as many mock Hubble Space Telescope (HST) images, SNPE matches the constraints on the low-mass halo population produced by our best non-sequential model. Our experiments suggest that an over three order-of-magnitude increase in training set size and GPU hours would be required to achieve an equivalent result without sequential methods. The notable improvement in constraining power enabled by our sequential approach highlights that the current constraints are limited primarily by methodology and not the data itself. Motivated by these results, we will show how these sequential methods can be further optimized to dynamically generate the “optimal” training set for a generic inference problem.
For help, please contact Webmaster.