Speaker
Description
Simulation-based inference (SBI) has become a key tool for hypothesis testing in high-energy particle physics, enabling likelihood ratio estimation directly from simulated data without requiring tractable likelihoods. In practice, SBI is often applied to high-level variables reconstructed from detector measurements as point estimates, discarding hypothesis-dependent information contained in the reconstruction uncertainty. A direct end-to-end SBI approach on raw detector observables avoids this loss but is often impractical due to high dimensionality, and requires full recalibration whenever the alternative hypothesis changes.
We present a new approach based on probabilistic reconstruction of the latent variables of interest. Rather than assigning a point estimate, we model the full posterior distribution of the latent variables conditioned on the detector observables. Combined with an SBI classifier in latent space, the resulting likelihood-ratio estimator is equivalent to end-to-end SBI while remaining computationally tractable. The generative component is calibrated only once under the null hypothesis and requires no further recalibration.
We demonstrate the framework on two problems: a supersymmetric top-quark pair production search using transformer-based flow matching for posterior reconstruction of invisible-particle kinematics, and an image reconstruction benchmark using convolutional flow matching. Across both benchmarks, increasing the number of posterior samples systematically closes the performance gap to end-to-end SBI while consistently outperforming conventional point-estimate reconstruction methods.