Toward Optimal Signal Extraction for Imaging X-Ray Polarimetry

We describe an optimal signal extraction process for imaging X-ray polarimetry using an ensemble of deep neural networks. The initial photoelectron angle, used to recover the polarization, has errors following a von Mises distribution. This is complicated by events converting outside of the fiducial gas volume, whose tracks have little polarization sensitivity. We train a deep ensemble of convolutional neural networks to select against these events and to measure event angles and errors for the desired gas-conversion tracks. We show how the expected modulation amplitude from each event gives an optimal weighting to maximize signal-to-noise ratio of the recovered polarization. Applying this weighted maximum likelihood event analysis yields sensitivity (MDP99) improvements of similar to 10% over earlier heuristic weighting schemes and mitigates the need to adjust said weighting for the source spectrum. We apply our new technique to a selection of astrophysical spectra, including complex extreme examples, and compare the polarization recovery to the current state of the art.

Automated summary

In this study, an optimal signal extraction process for imaging X-ray polarimetry was described using an ensemble of deep neural networks. The trained neural networks were able to select against certain events and measure event angles and errors for desired gas-conversion tracks, resulting in an improved signal-to-noise ratio for recovered polarization. The new technique showed sensitivity improvements over previous weighting schemes and eliminated the need to adjust weighting for the source spectrum.