Speaker
Description
Precise reconstruction of event kinematics in the presence of invisible particles constitutes a fundamental underconstrained inference problem in particle physics. In processes such as dileptonic $t\bar{t}$ production, multiple undetected neutrinos lead to a multimodal solution space, where several kinematically consistent configurations can explain a single observed event.
We present a preference-optimized generative framework for underconstrained inference, built on the event-level foundation model EveNet. Our approach augments a diffusion-based generative model with Direct Group Preference Optimization (DGPO), a post-training strategy that shifts the learned distribution toward physically preferred solutions while preserving its multimodal structure.
Evaluated on the $t\bar{t}$ dilepton channel, the preference-optimized model improves reconstruction fidelity and reduces unfolded uncertainties compared to both the baseline EveNet diffusion model and $\nu^2$-Flows across key observables. These results establish preference optimization as an effective paradigm for generative inference in underconstrained systems, with direct implications for precision measurements at the LHC.