Poster + Paper
31 August 2022 Validation of neural network software by using IXPE ground calibration data
Author Affiliations +
Conference Poster
Abstract
The Imaging X-ray Polarimetry Explorer (IXPE), launched 2021 December 9, will enable meaningful x-ray polarimetry of several types of astronomical sources. Aiming to improve the polarimetric sensitivity of Gas Pixel Detectors, track-reconstruction algorithms based upon machine learning have been proposed in the literature. In particular, a neural-network approach recently developed at Stanford University seems very promising. Here, we describe results obtained using this neural-network approach to analyze IXPE ground calibration data; we then compare those results with results obtained using the current moments-based analysis approach.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alessandro Di Marco, Allyn Tennant, Fabio La Monaca, Fabio Muleri, John Rankin, John Rushing, Paolo Soffitta, Giancarlo Baglioni, Luca Baldini, Enrico Costa, Kurt Dietz, Sergio Fabiani, Vittorio Latorre, Ugo Locatelli, Alberto Manfreda, Stephen O'Dell, Lawrence Peirson, and Martin Weisskopf "Validation of neural network software by using IXPE ground calibration data", Proc. SPIE 12181, Space Telescopes and Instrumentation 2022: Ultraviolet to Gamma Ray, 1218157 (31 August 2022); https://doi.org/10.1117/12.2628976
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KEYWORDS
Modulation

Calibration

Polarization

Polarimetry

X-rays

Sensors

Neural networks

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