具有振幅各向异性的二维物体的深度学习鬼偏振测量

IF 0.4 4区物理与天体物理 Q4 PHYSICS, MULTIDISCIPLINARY

Moscow University Physics Bulletin Pub Date : 2025-05-11 DOI:10.3103/S002713492570016X

D. A. Chernousov, D. P. Agapov

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引用次数: 0

摘要

本文讨论了深度学习在解决计算虚偏振反演问题中的潜力。首次证明了利用模型数据训练的神经网络可以恢复具有线性振幅各向异性的物体偏振特性的空间分布。确定了线性振幅各向异性参数的空间分布，方位角和各向异性值的精度分别为7.8 \(\%\)和15.6 \(\%\)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep Learning Ghost Polarimetry of Two-Dimensional Objects with Amplitude Anisotropy

This paper discusses the potential of deep learning in solving the inverse problem of computational ghost polarimetry. For the first time, it is demonstrated that the spatial distribution of the polarization properties of objects with linear amplitude anisotropy can be restored using a neural network trained on model data. The spatial distribution of the parameters of linear amplitude anisotropy is determined with an accuracy of 7.8\(\%\) and 15.6\(\%\) for the azimuth and the value of anisotropy, respectively.

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来源期刊

Moscow University Physics Bulletin PHYSICS, MULTIDISCIPLINARY-

CiteScore

0.70

自引率

0.00%

发文量

129

审稿时长

6-12 weeks

期刊介绍： Moscow University Physics Bulletin publishes original papers (reviews, articles, and brief communications) in the following fields of experimental and theoretical physics: theoretical and mathematical physics; physics of nuclei and elementary particles; radiophysics, electronics, acoustics; optics and spectroscopy; laser physics; condensed matter physics; chemical physics, physical kinetics, and plasma physics; biophysics and medical physics; astronomy, astrophysics, and cosmology; physics of the Earth’s, atmosphere, and hydrosphere.