We investigate the adoption of Machine Learning techniques for piston sensing in the context of segmented primary mirror telescopes by the means of numerical simulations. Considering a Natural Guide Star Wavefront Sensor, composed by one high order modes sensor plus a second sensor dedicated to the differential piston modes, we focus on the latter and tackle the problem of providing an accurate estimation for the piston modes coefficients from a defocused image of the system PSF.

We consider as a baseline algorithm a customized version of LIFT (which is based on a Maximum Likelihood Estimation) and compare its performance with a Deep Neural Network (DNN) regression. After considering several DNN architectures, we designed a simple one and performed some degree of hyperparameter optimization on it to obtain the final DNN version.