Light Scattering of Irregular Grains with Neural Networks with Zhé-Yǔ Daniel Lín

Abstract: Light scattering by dust particles is often modeled assuming the dust is spherical for numerical simplicity and speed. However, real dust particles have highly irregular morphologies that significantly affect their scattering properties. In this talk, I will introduce a case study in which we train a neural network on simulations of light scattering from irregularly shaped dust grains, offering a computationally efficient alternative to the Lorenz-Mie theory. We computed scattering properties using the Discrete Dipole Approximation code for irregularly shaped particles across a large range of sizes and complex refractive index, including astrosilicates, pyroxene, enstatite, water-ice, etc, in the mid-infrared. The neural network operates at millisecond timescales while maintaining superior accuracy compared to linear interpolation. Validation against laboratory measurements of forsterite and hematite demonstrates that our neural network captures both qualitative and quantitative behaviors more accurately than spherical models. Millimeter-wavelength applications reveal that spherical grains produce opposite polarization signatures compared to irregular grains. Emulators of dust scattering can alleviate the computational barrier to incorporating realistic grain morphologies in the dust inference and radiative transfer simulations for various astronomical environments where dust particles are unlikely spherical.

Speaker Bio: Zhé-Yǔ Daniel Lín graduated with a PhD in astronomy at the University of Virginia in 2024 and subsequently joined the Earth & Planets Laboratory of Carnegie Science in Washington, D.C. as a postdoctoral fellow. His research aims to understand planet formation and the evolution of planetary systems. Specifically, he focuses on utilizing polarization from the Atacama Large Millimeter/submillimeter Array and the NSF Very Large Array to inspect grain growth and grain alignment mechanisms. He complements observational work by employing advanced numerical techniques, including Monte Carlo radiation transfer, discrete dipole approximation, and machine learning, to maximize insights from data.