Speakers
Description
Collisions at the Large Hadron Collider (LHC) provide information about the values of parameters in theories of fundamental physics. Extracting measurements of these parameters requires accounting for effects introduced by the particle detector used to observe the collisions. The typical approach is to use a high-fidelity simulation of the detector to generate synthetic datasets that can then be compared directly with experimental data. However, these simulations are often proprietary and computationally expensive. An alternative approach, unfolding, statistically adjusts the experimental data for detector effects. Traditional unfolding algorithms require binning data in a small set of pre-selected dimensions. Recent methods using generative machine learning models have shown promise for performing un-binned unfolding in high dimensions, allowing later computation of many observables. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic $t\bar{t}$ production at the LHC. Additionally, the dependence of the unfolding on the training data prior is assessed by evaluating the model on datasets with alternative priors.
