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.

Validation of Neural Network software by using IXPE ground calibration data

Costa E.;Latorre V.;
2022-01-01

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
9781510653436
9781510653443
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/120889
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