Speaker
Description
Collider simulations simultaneously provide three sources of information: whether an event is signal or background, the physics parameters that generated each signal event, and the event kinematics from which signal regions are defined. Conventional pipelines use these sources in separate stages—training classifiers for signal discrimination, performing parameter inference for fixed analysis regions, and optimizing signal regions for specified signal hypotheses. Because all three are driven by the same simulation and the same underlying statistics, treating them as disjoint tasks leaves shared structure unexploited. We present a proof of concept in which a single region-aware model is trained jointly on all three sources. The same trained model then supports signal detection, parameter inference, and region screening as different projections of a shared conditional distribution, while providing a pretrained backbone that can be fine-tuned to a chosen region with less data than training from scratch. We validate the idea on a Gaussian bump-hunt, where all three tasks and fine-tuning behave largely as intended, and stress-test it on a non-resonant mono-jet dark-matter search that exposes its limitations. These results motivate unified learning from collider simulations as a precursor to foundation models for collider analysis.