In unfolding problem, the response matrix is the forward operator which models the detector response. In practice, the response matrix is not known analytically. Instead, it needs to be estimated using Monte Carlo simulation, which introduces statistical uncertainty into the unfolding procedure. This raises the question of how to estimate the response matrix in a sensible way. In most analyses...
In this study, the process of unfolding is studied in the context of a heavy ion photon-tagged jet analysis. The SVD and D'Agostini unfolding algorithms are compared, and an application of using the MSE to choose the regularization strength is shown. Additionally, the investigation looks into the bias associated with unfolding in relation to prior choice. The performance is evaluated with...
In High-Energy Physics (HEP) experiments, each measurement apparatus exhibit a unique signature in terms of detection efficiency, resolution, and geometric acceptance. The overall effect is that the distribution of each observable measured in a given physical process could be smeared and biased. Unfolding is the statistical technique employed to correct for this distortion and restore the...
Unfolding detector distortions in experimental data is critical for enabling precision measurements in high-energy physics (HEP). However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel unfolding approach using conditional denoising diffusion probabilistic models (cDDPM). By modeling the conditional probability...
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...
