Speaker
Description
Modern simulation-based inference (SBI) is a suite of tools to scaffold physics-based simulations with neural networks and other tools to perform inference using as much information as possible. We extend this toolkit to the case where some or all of the simulations are actually surrogate models learned directly from the data. This Surrogate Simulation-based Inference (S2BI) concept is studied in the context of resonant anomaly detection, where the background simulation is learned from sidebands. The signal simulation is also learned from data by extending the Residual Anomaly Detection with Density Estimation (R-ANODE) in the context of S2BI. A key challenge with S2BI is that the inference task can be distracted by inaccuracies of the simulation. This is especially acute in the flexible signal case, as the signal probability density can readily absorb artifacts from the learned background model. One solution is to encode prior information into the signal simulation, limiting flexibility enough to avoid absorbing background deficiencies, but still able to find anomalies. We study this idea by extending the Prior-Assisted Weak Supervision (PAWS) method in the context of S2BI. Overall, we find that both methods achieve excellent sensitivity and now allow for direct statistical analysis from their output. S2BI PAWS achieves discovery sensitivity down to an initial signal-to-noise ratio of 0.1. This performance, combined with its statistically robust and interpretable outputs, establishes a new state-of-the-art for anomaly detection sensitivity.
