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RSDs (Resistive AC-Coupled Silicon Detectors) are n-in-p silicon sensorsbased on the LGAD (Low-Gain Avalanche Diode) technology, featuring a continuousgain layer over the whole sensor area. The truly innovative feature of thesesensors is that the signal induced by an ionising particle is seen on severalpixels, allowing the use of reconstruction techniques that combine theinformation from many read-out channels. In this contribution, the firstapplication of a machine learning technique to RSD devices is presented. Thespatial resolution of this technique is compared to that obtained with thestandard RSD reconstruction methods that use analytical descriptions of thesignal sharing mechanism. A Multi-Output regressor algorithm, trained with acombination of simulated and real data, leads to a spatial resolution of lessthan 2 $\mu m$ for a sensor with a 100 $\mu m$ pixel. The prospects of futureimprovements are also discussed.

First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors