Bayesian analysis of signal deconvolution using measured instrument response functions
Pascal Pernot
Abstract
Using measured instrumental response functions for data deconvolution is a known source of uncertainty. This problem is revisited here with Bayesian data analysis an Monte Carlo simulations. Noise correlation induced by the convolution operator is identified as a major source of uncertainty which has been neglected in previous treatments of this problem. Application to a luminescence lifetime measurement setup shows that existing approximate treatments are markedly defficient and that the correlation length of the noise is directly related to the lifetime to be estimated. Simple counteractive treatments are proposed to increase the accuracy of this procedure.
Create a lesson
Related papers
Transient Supercriticality in Hawkes Processes: Detection, Identifiability, and Apparent Criticality
D. Sornette
Near-exact Upper Confidence Limits for Axion Haloscopes
Andrea Gallo Rosso, Jan Conrad
Track finding and particle identification performance with particle physics foundation models
Shuhang Li, Haiwang Yu, Gabor Galgoczi et al.
Accelerating fits in high-energy physics and beyond
Amr Elmarassy, Sebastian Schmitt, Eluned Smith
Sorted Mutability as an Entropic Functional: Theory and Two Applications
Michel Aguilera, Eugenio E. Vogel, Francisco J. Peña et al.
Data geometry preserves prediction but reshapes explanation
Nicola Amoroso, Mario Caruso, Marianna La Rocca et al.