Kavli Affiliate: Cheng Peng
| First 5 Authors: Yuan Ni, Yuan Ni, , ,
| Summary:
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.
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