Alladi Ramakrishnan Hall

Generative Unfolding at the LHC

Rahool Kumar Barman

IPMU, Tokyo, Japan

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.