The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding techniques while retaining the full dimensionality. Generative networks like invertible neural networks (INN) enable a probabilistic unfolding, which maps individual data events to their corresponding unfolded probability distribution.
The accuracy of such methods is however limited by how well the experimental data is modeled by the simulated training samples.
We introduce the iterative conditional INN (IcINN) for unfolding that adjusts for deviations between simulated training samples and data. The IcINN un-folding is first validated on toy data and then applied to pseudo-data for the pp → Zγγ process. Additionally, we validate the probabilistic unfolding with a novel approach using the traditional transfer matrix-based methods.
The general IcINN algorithm has been published in the paper arXiv:2212.08674. A second paper arXiv:2310.17037 which addresses probabilistic unfolding has recently been published as well.
