Determination of Particle-Size Distributions from Light-Scattering Measurement Using Constrained Gaussian Process Regression
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912466091376640 |
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| author | Seyedheydari, Fahime Nasiri, Mahdi Mińkowski, Marcin Särkkä, Simo |
| author_facet | Seyedheydari, Fahime Nasiri, Mahdi Mińkowski, Marcin Särkkä, Simo |
| contents | In this work, we propose a novel methodology for robustly estimating particle size distributions from optical scattering measurements using constrained Gaussian process regression. The estimation of particle size distributions is commonly formulated as a Fredholm integral equation of the first kind, an ill-posed inverse problem characterized by instability due to measurement noise and limited data. To address this, we use a Gaussian process prior to regularize the solution and integrate a normalization constraint into the Gaussian process via two approaches: by constraining the Gaussian process using a pseudo-measurement and by using Lagrange multipliers in the equivalent optimization problem. To improve computational efficiency, we employ a spectral expansion of the covariance kernel using eigenfunctions of the Laplace operator, resulting in a computationally tractable low-rank representation without sacrificing accuracy. Additionally, we investigate two complementary strategies for hyperparameter estimation: a data-driven approach based on maximizing the unconstrained log marginal likelihood, and an alternative approach where the physical constraints are taken into account. Numerical experiments demonstrate that the proposed constrained Gaussian process regression framework accurately reconstructs particle size distributions, producing numerically stable, smooth, and physically interpretable results. This methodology provides a principled and efficient solution for addressing inverse scattering problems and related ill-posed integral equations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_03736 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Determination of Particle-Size Distributions from Light-Scattering Measurement Using Constrained Gaussian Process Regression Seyedheydari, Fahime Nasiri, Mahdi Mińkowski, Marcin Särkkä, Simo Machine Learning Optics Methodology In this work, we propose a novel methodology for robustly estimating particle size distributions from optical scattering measurements using constrained Gaussian process regression. The estimation of particle size distributions is commonly formulated as a Fredholm integral equation of the first kind, an ill-posed inverse problem characterized by instability due to measurement noise and limited data. To address this, we use a Gaussian process prior to regularize the solution and integrate a normalization constraint into the Gaussian process via two approaches: by constraining the Gaussian process using a pseudo-measurement and by using Lagrange multipliers in the equivalent optimization problem. To improve computational efficiency, we employ a spectral expansion of the covariance kernel using eigenfunctions of the Laplace operator, resulting in a computationally tractable low-rank representation without sacrificing accuracy. Additionally, we investigate two complementary strategies for hyperparameter estimation: a data-driven approach based on maximizing the unconstrained log marginal likelihood, and an alternative approach where the physical constraints are taken into account. Numerical experiments demonstrate that the proposed constrained Gaussian process regression framework accurately reconstructs particle size distributions, producing numerically stable, smooth, and physically interpretable results. This methodology provides a principled and efficient solution for addressing inverse scattering problems and related ill-posed integral equations. |
| title | Determination of Particle-Size Distributions from Light-Scattering Measurement Using Constrained Gaussian Process Regression |
| topic | Machine Learning Optics Methodology |
| url | https://arxiv.org/abs/2507.03736 |