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.
In this talk, I will present our work on relating atomic-level protein dynamics to its functions, e.g., biological signaling. Cells and organisms react to external and internal signals by proteins that consist of sensor and effector modules. Sensors detect environmental changes, such as light, pH, hormones, etc., while corresponding effectors trigger a response. First, I will discuss the initial step for signaling, i.e., drug molecules binding to proteins. I will exemplify this with our recent simulation and theoretical modeling efforts in collaboration with experimentalists to understand polyelectrolyte–protein interactions and help design polymers for SARS-CoV-2 virus inhibition. Later, I will introduce basic statistical-mechanics concepts to derive transmit functions that describe how a local time-dependent perturbation, which can be a deformation or a force, propagates in a viscoelastic medium such as a protein. Transmit functions are defined by equilibrium fluctuations fromsimulations or experimental observations. We apply this framework to our molecular dynamics simulation data of a bacterial signaling protein, for quantifying signal transfer efficiency of its principal deformation modes, namely shift, splay, and twist. Finally, I will conclude the talk with a few future research directions.