A deep ensemble approach to X-ray polarimetry
This work addresses a bottleneck in X-ray astronomy for researchers using NASA's IXPE mission, offering a significant performance improvement but is incremental as it builds on existing deep learning techniques.
The paper tackles the problem of limited sensitivity in X-ray polarimetry due to suboptimal track reconstruction algorithms by introducing a deep ensemble method with weighted maximum likelihood, resulting in a ~40% decrease in required exposure times for a given signal-to-noise ratio.
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.