Deep ensemble analysis for Imaging X-ray Polarimetry

• Published in: Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Accelerators, spectrometers, detectors and associated equipment, 2021-01, Vol.986, p.164740, Article 164740
• Main Authors: Peirson, A.L., Romani, R.W., Marshall, H.L., Steiner, J.F., Baldini, L.
• Format: Article
• Language: English
• Subjects: Deep learning; Gas pixel detector; IXPE; Machine learning; Polarization; X-ray polarimeter
• ISSN: 0168-9002; 1872-9576
• DOI: 10.1016/j.nima.2020.164740

We present a method for enhancing the sensitivity of X-ray telescopic observations with imaging polarimeters, with a focus on the gas pixel detectors (GPDs) to be flown on the Imaging X-ray Polarimetry Explorer (IXPE). Our analysis determines photoelectron directions, X-ray absorption points and X-ray energies for 1-9keV event tracks, with estimates for both the statistical and model (reconstruction) uncertainties. We use a weighted maximum likelihood combination of predictions from a deep ensemble of ResNet convolutional neural networks, trained on Monte Carlo event simulations. We define a figure of merit to compare the polarization bias–variance trade-off in track reconstruction algorithms. For power-law source spectra, our method improves on the current planned IXPE analysis (and previous deep learning approaches), providing ∼45% increase in effective exposure times. For individual energies, our method produces 20%–30% absolute improvements in modulation factor for simulated 100% polarized events, while keeping residual systematic modulation within 1σ of the finite sample minimum. Absorption point location and photon energy estimates are also significantly improved. We have validated our method with sample data from real GPD detectors.