| Speaker: | Rahool Kumar Barman (Kavli IPMU) |
|---|---|
| Title: | Returning CP-observables to the frames they belong |
| Date (JST): | Wed, Dec 13, 2023, 13:30 - 15:00 |
| Place: | Seminar Room A |
| Abstract: | Unfolding techniques allow an inversion of the event simulation chain or deconvoluting the data reconstructed at the LHC, making it possible to infer the corresponding parton level phase where new physics effects are maximally encoded. In this talk, I will discuss the various Machine Learning-based generative models capable of performing binning-independent and multi-dimensional unfolding. Optimal kinematic observables are often defined in specific frames and then approximated at the reconstruction level. I will discuss how multi-dimensional unfolding methods allow us to reconstruct these observables in their proper rest frame and probabilistically faithful way. I will discuss the case study of measuring the CP-phase in the top Yukawa coupling using conditional Invertible Neural Networks. Our method uses key advantages of generative unfolding, but as a constructed observable, it fits into standard LHC analysis frameworks. |
