Building a reliable simulator at scale, the case of Rubin Supernova Cosmology

Performing Simulation-Based Inference requires reliable simulators of a vast array of physical effects in any modern experimental setting. I will discuss two tools for achieving such reliability. First, I discuss caskade, which is designed for arbitrarily scalable simulator design. I will demonstrate compelling applications in astronomical image processing, strong gravitational lensing, and supernova cosmology. Second, I will discuss PTED, a tool for evaluating simulated samples against real data. It is an exact, multi-dimensional, two sample test with a high sensitivity to distribution mismatch. We will see how it effectively spots common errors in simulation products. Further, I will show how it can evaluate the final product of SBI, posterior samples, in full multi-dimensional detail. Finally, I will end with some preliminary results using these tools on the case of Rubin Observatory Supernova cosmology at the scale of hundreds of thousands of objects.

Simulation-Based Inference

Simulators

Two-Sample Tests

Python

Rubin Observatory