  X-ray polarimetry will soon open a new window on the high energy universe
with the launch of NASA's Imaging X-ray Polarimetry Explorer (IXPE).
Polarimeters are currently limited by their track reconstruction algorithms,
which typically use linear estimators and do not consider individual event
quality. We present a modern deep learning method for maximizing the
sensitivity of X-ray telescopic observations with imaging polarimeters, with a
focus on the gas pixel detectors (GPDs) to be flown on IXPE. We use a weighted
maximum likelihood combination of predictions from a deep ensemble of ResNets,
trained on Monte Carlo event simulations. We derive and apply the optimal event
weighting for maximizing the polarization signal-to-noise ratio (SNR) in track
reconstruction algorithms. For typical power-law source spectra, our method
improves on the current state of the art, providing a ~40% decrease in required
exposure times for a given SNR.
