Neural calibration of imaging Stokes polarimeters

F. A. IGLESIAS; A. ASENSIO-RAMOS; M. SANCHEZ; A. FELLER

San Juan

Conferencia; 65va Reunión Anual de la AAA; 2023

Asociación Argentina de Astronomía

Current polarimetric calibration techniques derive the instrument modulation matrix by numerically fitting an instrumental model to measurements of a set of calibration Stokes vectors. These techniques are typically limited to an error on the retrieved normalized Stokes Q, U and V parameters, in the 10-2 to 10-3 range. This error commonly increases when the instrument response varies considerably across its field of view and/or when instrumental effects are present, which are not included in the assumed calibration model, such as camera non-linearity or unknowns in the optical setup. In this work, we propose a new technique to calibrate imaging Stokes polarimeters based on a model composed of fully-connected, multi-layer neural networks (NN's). This NN model is trained to learn the instrument modulation matrix given a set of input parameters, e.g., position in the field of view, using the same calibration data that is acquired for the current techniques. The main advantage of our NN-based approach is its flexibility to incorporate instrumental effects for which no accurate model is available and, possibly through fusion of data from other types of relevant calibrations, obtain a more accurate instrument response. We present a preliminary result of our model performance using synthetic calibration and solar data, where the ground truth is known.