A deep ensemble approach to X-ray polarimetry
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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2021
- DOI:
- 10.48550/arXiv.2111.03047
- arXiv:
- arXiv:2111.03047
- Bibcode:
- 2021arXiv211103047P
- Keywords:
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- Astrophysics - Instrumentation and Methods for Astrophysics;
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning
- E-Print:
- Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021)