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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
