A deep ensemble approach to X-ray polarimetry

TL;DR

This paper introduces a deep ensemble learning method using ResNets to enhance X-ray polarimetry sensitivity, significantly reducing exposure times needed for high SNR measurements in upcoming space missions.

Contribution

It presents a novel deep learning approach with ensemble predictions and optimal event weighting to improve track reconstruction in X-ray polarimeters, outperforming existing algorithms.

Findings

Achieves approximately 40% reduction in required exposure time for given SNR.

Demonstrates improved polarization signal-to-noise ratio with deep ensemble method.

Provides a new framework for maximizing sensitivity in X-ray polarimetry observations.

Abstract

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.