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arXiv 2409.08007astro-ph.HE
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MLody——深度学习生成的偏振同步辐射系数

MLody -- Deep Learning Generated Polarized Synchrotron Coefficients

Jordy Davelaar

AI总结:

本文提出深度神经网络 MLody,用于在宽等离子体参数范围内高精度计算偏振同步辐射系数;黑洞合成图像显示其线偏振和圆偏振结果相较传统拟合可相差两倍,有望提升 EHT 参数估计精度。

摘要:

偏振同步辐射发射是高能天体物理学中的一个基本过程,尤其存在于黑洞和脉冲星周围的环境中。对这种发射进行精确建模需要精确计算发射、吸收、旋转和转换系数,这些系数对于辐射转移模拟至关重要。传统上,这些系数是利用基于预先计算的真值的拟合函数推导出来的。然而,这些拟合函数往往缺乏精度,尤其是在生成它们所用数据集中未能充分体现的特定等离子体条件下。在这项工作中,我们介绍了 ${\tt MLody}$,这是一种深度神经网络,旨在在广泛的等离子体参数范围内以高精度计算偏振同步辐射系数。我们通过将 ${\tt MLody}$ 与辐射转移代码集成,为吸积黑洞模拟生成合成偏振同步辐射图像,展示了其能力。我们的结果显示,与传统方法相比,线性和圆偏振均存在显著差异,最高可达两倍。这些差异可能对事件视界望远镜(Event Horizon Telescope)观测中的参数估计产生重要影响,表明 ${\tt MLody}$ 可以提高未来天体物理分析的准确性。

英文摘要:

Polarized synchrotron emission is a fundamental process in high-energy astrophysics, particularly in the environments around black holes and pulsars. Accurate modeling of this emission requires precise computation of the emission, absorption, rotation, and conversion coefficients, which are critical for radiative transfer simulations. Traditionally, these coefficients are derived using fit functions based on precomputed ground truth values. However, these fit functions often lack accuracy, particularly in specific plasma conditions not well represented in the datasets used to generate them. In this work, we introduce ${\tt MLody}$, a deep neural network designed to compute polarized synchrotron coefficients with high accuracy across a wide range of plasma parameters. We demonstrate ${\tt MLody}$'s capabilities by integrating it with a radiative transfer code to generate synthetic polarized synchrotron images for an accreting black hole simulation. Our results reveal significant differences, up to a factor of two, in both linear and circular polarization compared to traditional methods. These differences could have important implications for parameter estimation in Event Horizon Telescope observations, suggesting that ${\tt MLody}$ could enhance the accuracy of future astrophysical analyses.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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