Speaker
Description
The unfolding problem is to make inferences about the true particle spectrum based on smeared observations from a detector. This is an ill-posed inverse problem, where small changes in the smeared distribution can lead to large fluctuations in the unfolded distribution. The forward operator is the response matrix which models the detector response. In practice, the forward operator is rarely known analytically and is instead estimated using Monte Carlo simulation. This raises the question of how to best estimate the response matrix and what impact this estimation has on the unfolded solutions. In most analyses at the LHC, response matrix estimation is done by binning the true and smeared events and counting the propagation of events between the bins. Unexpectedly, we find that the noise in the estimated response matrix can inadvertently regularize the problem. As an alternative, we propose to use conditional density estimation to estimate the response kernel in the unbinned space followed by binning this estimator. Using a simulation study, we investigate the performance of the two approaches. Finally, we discuss how a new class of unfolding techniques might eliminate the need for plug-in response matrix estimation, hence simplifying this aspect of the unfolding problem.
