Speaker
Nityaansh Parekh
(Michigan State University (US))
Description
CP-sensitive observables in H+2jet production, such as the azimuthal angle between the leading jets, are defined at parton level but measured at detector level, requiring unfolding to recover them. We present a conditional invertible neural network (cINN) that learns a full posterior over parton-level kinematics given detector-level observables trained jointly across multiple EFT coupling scenarios. We discuss design choices driven by the CP-sensitive target observable and progress toward a general, data-ready unfolding pipeline.
Authors
Kirtimaan Mohan
(Michigan State University)
Nityaansh Parekh
(Michigan State University (US))