  To achieve state-of-the-art jet energy resolution for Particle Flow,
sophisticated energy clustering algorithms must be developed that can fully
exploit available information to separate energy deposits from charged and
neutral particles. Three published neural network-based shower separation
models were applied to simulation and experimental data to measure the
performance of the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL)
technological prototype in distinguishing the energy deposited by a single
charged and single neutral hadron for Particle Flow. The performance of models
trained using only standard spatial and energy and charged track position
information from an event was compared to models trained using timing
information available from AHCAL, which is expected to improve sensitivity to
shower development and, therefore, aid in clustering. Both simulation and
experimental data were used to train and test the models and their performances
were compared. The best-performing neural network achieved significantly
superior event reconstruction when timing information was utilised in training
for the case where the charged hadron had more energy than the neutral one,
motivating temporally sensitive calorimeters. All models under test were
observed to tend to allocate energy deposited by the more energetic of the two
showers to the less energetic one. Similar shower reconstruction performance
was observed for a model trained on simulation and applied to data and a model
trained and applied to data.
