Speaker
Description
Open statistical models released by the LHC experiments are transforming the reinterpretation of collider searches by providing access to the full likelihood information of experimental analyses. However, evaluating these likelihoods remains computationally expensive, particularly for analyses with many signal and control regions, limiting their use in large-scale phenomenological studies. In this contribution, we present the Open Library of Learned Likelihoods (OLLL), a collection of neural-network surrogates for profiled likelihoods from public LHC statistical models. The surrogate models are trained on likelihood evaluations generated with pyhf, optimized using a heteroscedastic loss that enables them to predict both the profiled likelihood and an associated uncertainty estimate, and serialized in the framework-independent ONNX format for seamless integration into reinterpretation frameworks. We demonstrate the approach on five ATLAS SUSY searches of increasing complexity, where the learned likelihoods accurately reproduce both the official ATLAS and full pyhf exclusion contours while reducing the computational cost of likelihood evaluation by orders of magnitude. The resulting library provides a practical and extensible infrastructure for fast and statistically faithful reinterpretation of LHC searches.