Speaker
Description
Airless Solar System objects’ photometric phase curves exhibit a distinctive opposition effect, marked by nonlinear brightening as phase angles approach the backscattering direction. In addition to phase angles below approximately 20 degrees, polarimetric phase curves predominantly show a negative degree of linear polarization, with scattered light polarized parallel to the Sun-object-observer plane of scattering. These phenomena arise from electromagnetic wave scattering in discrete media composed of small particles, due to the interference between reciprocal rays that traverse identical optical paths in opposite directions. As such, this coherent backscattering effect makes the opposition phenomena dependent on specific properties of the medium, particularly the particle size, refractive index, shape, and the packing density of the medium. Incorporating coherent backscattering (CB) into radiative transfer (RT) models provides a comprehensive modeling solution. In addition to coherent backscattering, nonspherical particles contribute to the negative degree of linear polarization.
In prior research, we modeled polarimetric phase curves for Jupiter’s satellites Europa, Ganymede, and Io [1,2]. We employ radiative-transfer coherent-backscattering (RT-CB, [3,4]) modeling with an ensemble-averaged scattering matrix. This approach utilizes parameterized matrix elements to replicate the observed small-phase-angle polarimetric phase curves for these objects [1]. Decomposing the ensemble-averaged scattering matrix into polarization-conserving Mueller matrices [4] enables RT-CB computations for discrete random media with nonspherical particles [5]. This decomposition facilitates conclusions about near-surface structure and composition by comparing the RT-CB model results with observations [6].
In our ongoing work we replace the previously used ensemble-averaged scattering matrices with those derived from a near-surface composition model characterized by physically described properties, including a size distribution of randomly shaped and oriented particles. These particle geometries are generated using a 3-D Voronoi diagram, with monomers of effective sizes ranging from 0.1 to 0.5 microns. These geometries are then used as inputs for Advanced Discrete Dipole Approximation (ADDA) [7] simulations and complemented by larger Gaussian particles created with the SIRIS-4 [8,9] code, resulting in an ensemble-averaged scattering matrix that reflects a physically motivated near-surface composition.
By simulating light scattering from a near-surface composition model with specified physical properties and comparing the results with an ensemble-averaged scattering matrix, we can gain insight into the compositional characteristics of icy satellites—such as particle size distribution, packing density, and mineral composition. The RT-CB model, when combined with photometric and polarimetric measurements, thus provides a valuable tool for characterizing icy satellites and other airless bodies based on both ground-based observations and in-situ measurement.
References:
[1] N. Kiselev et al., ”New Polarimetric Data for the Galilean Satellites: Europa Observations and Modeling” Planet. Sci. J. 3, 134 (2022)
[2] N. Kiselev et al., ”New Polarimetric Data for the Galilean Satellites: Io and Ganymede Observations and Modeling,” Planet. Sci. J. 5, 10 (2024)
[3] K. Muinonen et al., ”Coherent Backscattering Verified Numerically for a Finite Volume of Spherical Particles,” ApJ 760, 118 (2012)
[4] K. Muinonen, A. Penttilä, ”Scattering matrices of particle ensembles analytically decomposed into pure Mueller matrices,” JQSRT 324, (2024)
[5] K. Muinonen et al., ”Coherent backscattering in discrete random media of particle ensembles,” JQSRT 330, (2025)
[6] A. Leppälä et al., in preparation, (2026)
[7] M. A. Yurkin, A. G. Hoekstra, ”The discrete-dipole-approximation code ADDA: Capabilities and known limitations,”JQSRT 112, 13 (2011)
[8] K. Muinonen et al., ”Light scattering by Gaussian particles with internal inclusions and roughened surfaces using ray optics,” JQSRT 110 (2012)
[9] H. Lindqvist et al., ”Ray optics for absorbing particles with application to ice crystals at near-infrared wavelengths,” JQSRT 217, 118 (2018)