The conventional approach to LHC analysis involves comparing
the measured data to Monte Carlo simulations. These simulations start
at the hard-scattering level, where the potential for new physics is
maximal, and proceed through various stages, including showering,
hadronization, and detector response. Unfortunately, each stage
introduces complexities, resulting in a convoluted representation of
the true underlying physics at the simulated detector level. Events
measured at the LHC detector are also a somewhat convoluted version of
the true underlying physics, due to various latent effects.
Eliminating these convolutions is essential for a direct comparison
between theoretical predictions and measured data, which can
be achieved through the process of 'Unfolding', where reconstructed or
measured events are directly mapped to the hard-scattering level.
In this seminar, I will discuss the development and application of
multi-dimensional unfolding models that utilize machine-learning-based
generative techniques, specifically Generative Adversarial Networks
and Normalizing Flows. A key focus will be on how multi-dimensional
unfolding with NFs allows the reconstruction of observables in their
proper rest frame and in a probabilistically faithful way. I will
highlight its practical impact through a case study on the measurement
of CP-phase in the top Yukawa coupling.