Parameterization of Photon Yields In Idealized Ice for Pixelated Reconstruction

IceCube Neutrino Observatory currently uses single-PMT Digital Optical Modules (DOMs) for data collection. Future expansions (IceCube-Upgrade, IceCube-Gen2) will enhance detection by employing multi-PMT DOMs, improving sensitivity and accuracy in locating neutrino sources. However, the adoption of multi-pixel sensors presents challenges due to high memory usage. Utilizing deep neural networks relies on generating extensive Monte Carlo datasets using GPUs. To address this, we propose a maximum likelihood method, analytically parameterizing photon propagation in a homogenous medium for arrival timing and flux distribution. For a given detector geometry and DOM PMT configuration, events were simulated by Poisson sampling from the parameterization. We show preliminary results for idealized angular resolution gain by utilization of pixelated sensors.