Hamiltonian Monte Carlo methods for spectroscopy data analysis

TL;DR

This paper introduces a scalable Bayesian framework utilizing Hamiltonian Monte Carlo algorithms for high-fidelity analysis of confocal fluorescence spectroscopy data, enabling superresolution trajectory inference beyond native data resolution.

Contribution

The work develops novel HMC algorithms tailored for spectroscopy data, including a semi-implicit integrator to handle stiff differential equations, improving efficiency and accuracy over existing methods.

Findings

Enhanced trajectory estimation with superresolution parameters.

Stable HMC sampling with larger step sizes using semi-implicit integrator.

Framework adaptable to complex molecular motion models.

Abstract

We present a scalable Bayesian framework for the analysis of confocal fluorescence spectroscopy data, addressing key limitations in traditional fluorescence correlation spectroscopy methods. Our framework captures molecular motion, microscope optics, and photon detection with high fidelity, enabling statistical inference of molecule trajectories from raw photon count data, introducing a superresolution parameter which further enhances trajectory estimation beyond the native time resolution of data acquisition. To handle the high dimensionality of the arising posterior distribution, we develop a family of Hamiltonian Monte Carlo (HMC) algorithms that leverages the unique characteristics inherent to spectroscopy data analysis. Here, due to the highly-coupled correlation structure of the target posterior distribution, HMC requires the numerical solution of a stiff ordinary differential equation containing a two-scale discrete Laplacian. By considering the spectral properties of this operator, we produce a CFL-type integrator stability condition for the standard St\"ormer-Verlet integrator used in HMC. To circumvent this instability we introduce a semi-implicit (IMEX) method which treats the stiff and non-stiff parts differently, while leveraging the sparse structure of the discrete Laplacian for computational efficiency. Detailed numerical experiments demonstrate that this method improves upon fully explicit approaches, allowing larger HMC step sizes and maintaining second-order accuracy in position and energy. Our framework provides a foundation for extensions to more complex models such as surface constrained molecular motion or motion with multiple diffusion modes.