标题： 基于物理引导的双隐式神经表示的源分离 显示英文标题

标题： Physics-Guided Dual Implicit Neural Representations for Source Separation

摘要： 在大多数先进的实验和观测技术中，高效的数据分析存在重大挑战，因为收集到的信号通常包含不需要的贡献——如背景和信号失真——这些可能会掩盖感兴趣的实际相关信息。 为了解决这个问题，我们开发了一种自监督的机器学习方法，使用双隐式神经表示框架进行源分离，该框架联合训练两个神经网络：一个用于近似感兴趣物理信号的失真，另一个用于学习有效的背景贡献。 我们的方法直接从原始数据中学习，通过最小化基于重建的损失函数，而无需标记数据或预定义的字典。 我们通过考虑一个具有挑战性的案例研究来证明我们框架的有效性，该案例研究涉及四维参数空间中的大规模模拟以及实验动量-能量依赖的非弹性中子散射数据，其特征是异质背景贡献和对目标信号的未知失真。 该方法被发现能够在信号特性跨越参数空间所有四个维度的情况下，成功地从复杂或结构化的背景中分离出物理上有意义的信号。 提出了一种解析方法，用于指导正则化参数的选择。 我们的方法提供了一个多功能的框架，用于解决跨不同领域的源分离问题，从天文学测量中的叠加信号到生物医学图像重建中的结构特征。 显示英文摘要

摘要： Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions--such as background and signal distortions--that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated as well as experimental momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.