Towards Optimal Signal Extraction for Imaging X-ray Polarimetry

Abstract

We describe an optimal signal extraction process for imaging X-ray polarimetry using an ensemble of deep neural networks. The initial photo-electron 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 ~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.

Accepted for publication in ApJ. 12 pages, 9 figures

References in corpus (3)

• Analyzing the Data from X-ray Polarimeters with Stokes Parameters
• The Polarization Behavior of Relativistic Synchrotron Self-Compton Jets
• Multiband Weighting of X-ray Polarization Data

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• Polarisation Leakage due to Errors in Track Reconstruction in Gas Pixel Detectors
• Polarization Leakage and the IXPE PSF
• Joint machine learning and analytic track reconstruction for X-ray polarimetry with gas pixel detectors
• Analysis of the data from photoelectric gas polarimeters
• Testing High-Energy Emission Models for Blazars with X-ray Polarimetry
• A deep ensemble approach to X-ray polarimetry