Validation of Neural Network software by using IXPE ground calibration data

被引：0

作者：

Di Marco, Alessandro ^{[1
]}

Tennant, Allyn F. ^{[2
]}

La Monaca, Fabio ^{[1
]}

Muleri, Fabio ^{[1
]}

Rankin, John ^{[1
]}

Rushing, John ^{[3
]}

Soffitta, Paolo ^{[1
]}

Baglioni, Giancarlo ^{[4
]}

Baldini, Luca ^{[5
]}

Costa, Enrico ^{[1
]}

Dietz, Kurtis ^{[2
]}

Fabiani, Sergio ^{[1
]}

Latorre, Vittorio ^{[1
]}

Locatelli, Ugo ^{[4
]}

Manfreda, Alberto ^{[5
]}

O'Dell, Stephen L. ^{[2
]}

Peirson, Lawrence ^{[6
,7
]}

Weisskopf, Martin C. ^{[2
]}

机构：

[1] Ist Nazl Astrofis IAPS, Via Fosso Cavalliere 100, I-00133 Rome, Italy

[2] NASA Marshall Space Flight Ctr, Huntsville, AL 35812 USA

[3] Univ Alabama, Informat Technol & Syst Ctr, Huntsville, AL 35899 USA

[4] Univ Roma Tor Vergata, Dipartimento Matemat, Via Ric Sci 1, I-00133 Rome, Italy

[5] Ist Nazl Fis Nucl, Sez Pisa, Largo B Pontecorvo 3, I-56127 Pisa, Italy

[6] Stanford Univ, Dept Phys, Stanford, CA 94305 USA

[7] Stanford Univ, Kavli Inst Particle Astrophys & Cosmo, Stanford, CA 94305 USA

来源：

SPACE TELESCOPES AND INSTRUMENTATION 2022: ULTRAVIOLET TO GAMMA RAY | 2022年 / 12181卷

基金：

美国国家航空航天局;

关键词：

IXPE; X-rays; polarimetry; Neural networks; Machine Learning; GPD; X-RAY POLARIMETER; POLARIZATION;

D O I：

10.1117/12.2628976

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

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.

引用

页数：14

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