对称感知深度神经网络用于固体中的高波谱学
Optics express
|June 29, 2023
概括
深度神经网络现在可以解码固体中的超快量子动力学. 这种机器学习方法准确地分析噪声光谱,使得激光脉冲和量子系统的完整表征成为可能.
科学领域:
- 量子物理学的量子物理学
- 材料科学是一种材料科学.
- 机器学习 机器学习
背景情况:
- 神经网络在模式识别方面表现出色,但在超快速量子动力学方面未得到充分利用.
- 解读由强烈激光场驱动的复杂量子现象仍然具有挑战性.
研究的目的:
- 应用深度神经网络来分析模拟的非线性光学反应的噪声光谱.
- 证明网络能够从复杂的数据中恢复系统参数和激光脉冲特征.
主要方法:
- 使用标准深度神经网络进行光谱分析.
- 采用1D系统作为神经网络的培训"幼儿园".
- 应用重新训练的网络来分析在强烈的几周期激光脉冲下模拟的2D间隙石墨烯光谱.
主要成果:
- 神经网络成功地恢复了2D间隙石墨烯的参数化带结构.
- 尽管有噪音,但可以准确地检索发生的几周期脉冲的光谱相.
- 证明了对显著的振幅噪声和相位震荡的强度.
结论:
- 神经网络为固体中每秒高波谱学提供了可行的途径.
- 能够对短周期激光脉冲进行全光学,固态的表征,包括非线性光谱和载体外相.
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