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May 8 – 12, 2023
Norfolk Waterside Marriott
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Simulation and Reconstruction of Photoelectric X-ray Polarimetry on LPD

May 8, 2023, 2:45 PM
Chesapeake Meeting Room (Norfolk Waterside Marriott)

Chesapeake Meeting Room

Norfolk Waterside Marriott

235 East Main Street, Norfolk, VA, 23510
Oral Track 3 - Offline Computing Track 3+9 Crossover


Dr Yi, Difan (University of Chinese Academy of Sciences)


The Large Field Low-energy X-ray Polarization Detector (LPD) is a gas photoelectric effect polarization detector designed for the detailed study of X-ray temporary sources in high-energy astrophysics. Previous studies have shown that the polarization degree of gamma ray bursts (GRBs) is generally low or unpolarized. Considering the spatial background and other interferences, We need high modulation algorithms to observe low polarization GRB. For this purpose, moment analysis, graph theory, neural network algorithms are studied for the reconstruction of photoelectron emission angle. Combined with experimental and simulation data, the reconstruction performance of different algorithms at various energy and incident angles is evaluated.

Moment analysis algorithm finds out the large angle scattering point of photoelectron and remove the zone. Photoelectron track after cutting can be reconstructed. However, on the one hand, when track length is large, the performance of moment analysis algorithm becomes worse. On the other hand, for short cases, the track information loss caused by cutting is more serious, and the performance of moment analysis algorithm will also be degraded. In order to address these problems, graph theory algorithm and neural network are studied. Graph theory algorithm improves the reconstruction performance by precisely positioning the photoelectric action point through the trunk endpoint, which is more effective for longer tracks. Training samples of neural network algorithm are from the simulation platform built based on Geant4 in which photoelectric interaction, ionization diffusion, signal digitization and other processes on the detector are simulated as real as possible. Two typical neural networks, CNN and GNN, are studied. The results show that both neural networks predict high modulation and stability in designed energy range. In order to carefully evaluate the performance of the algorithm, the simulation should be as close to the real situation as possible.

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Primary authors

Dr Yi, Difan (University of Chinese Academy of Sciences) Prof. Liu, Hongbang (GuangXi University) Prof. Liu, Qian (University of Chinese Academy of Sciences) Dr Feng, Huanbo (Guangxi University) Dr Ma, Ruiting (University of Chinese Academy of Sciences) Prof. Xie, Fei (GuangXi University) Dr Feng, Zuke (GuangXi University)

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