How to infer the next Standard Model from LHC data?

Abstract: Despite compelling evidence for physics beyond the Standard Model (BSM) from cosmology, astrophysics, and theoretical considerations, no clear signal has emerged from LHC data so far. Most of the LHC searches focus on exclusive channels, targeting for excesses in specific final states. However, new physics may instead manifest as a distributed signal across many channels, evading traditional search strategies. It therefore becomes increasingly relevant to attempt a more global approach to testing BSM scenarios, in which information from multiple analyses is combined in a systematic way. In this talk, I will review recent developments in this direction, focusing on frameworks that enable the reinterpretation and statistical combination of collider searches over broad classes of models. I will then present a novel algorithm designed to identify evidence for new physics in such dispersed signatures, with minimal theoretical bias. This approach can be viewed as a step toward a more data-driven inference of the structure of physics beyond the Standard Model.