Calibration of nEXO light response.

nEXO is a proposed 5 tonne liquid xenon experiment which seeks to detect \gls{onbb} in Xe-136 using \gls{tpc} technology. The experiment will use the combina tion of scintillation and ionization signals to reconstruct events with an energy resolution of $<$1\% $\sigma/E$ at the \gls{onbb} Q-value. The scintillati on light will be collected by silicon photomultipliers (SiPMs) around the sides of the detector, and their collection efficiency will vary as a function of ev ent position. Using nEXO Monte Carlo simulation data, we have demonstrated the use of machine learning techniques to effectively map and correct the light re sponse of the detector while minimizing calibration time. In this talk, we present a strategy for calibrating the light response in the nEXO detector using alpha decays from ${}^{222}$Rn calibration source dissolved into the liquid xenon.