2026-06-27T23:49:47Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1288852025-12-13T00:45:23Zcom_20.500.14352_14col_20.500.14352_15

Suárez Bermejo, Juan Carlos Gorgas García, Francisco Javier Pascual Ramírez, Sergio Santarsiero, Massimo González de Sande, Juan Carlos Piquero Sanz, Gemma María 2025-12-12T18:16:22Z 2025-12-12T18:16:22Z 2024-01 J.C. Suárez-Bermejo, J. Gorgas, S. Pascual, M. Santarsiero, J.C.G. De Sande, G. Piquero, Bayesian inference approach for Full Poincaré Mueller polarimetry, Optics & Laser Technology 168 (2024) 109983. https://doi.org/10.1016/j.optlastec.2023.109983. 0030-3992 10.1016/j.optlastec.2023.109983 https://hdl.handle.net/20.500.14352/128885 1879-2545 https://dx.doi.org/10.1016/j.optlastec.2023.109983 https://www.sciencedirect.com/science/article/pii/S0030399223008769 Full Poincare Mueller Polarimetry is a new technique for characterizing samples by means of their Mueller matrix. The method is based on the use of a full Poincare beam as a generator of polarization states. These beams present different polarization states, covering the entire Poincare sphere surface, at different points in the beam cross section. To obtain the Mueller matrix, Stokes parameters are collected at both the entrance and the output of the sample. They are calculated from irradiance measurements at each pixel of a CCD camera for different configurations of the polarization state analyzer. These measurements can be processed in several ways. In this work, we propose to use Bayesian inference, in particular, Markov chain Monte Carlo methods, to obtain, without any prior knowledge of the sample, its Mueller matrix together with its uncertainties. The new approach is tested with experimental measurements of different samples and compared with the real theoretical Mueller matrices. Excellent agreement is observed between the experimental results and the theoretical ones for all the samples tested. eng open access Bayesian inference approach for Full Poincaré Mueller polarimetry journal article