Abstract
Studying the spectra of dust in different planetary environments is important asit provides valuable insights into the processes occurring in various astronomical
environments. This knowledge not only advances our understanding of other worlds
but also has practical implications for future space missions and our broader under-
standing of planetary systems. Spectral data often contains noise and one of the
challenges is to reduce this noise and improve the signal-to-noise ratio in order to
extract reliable information from the data. We explore and apply several background
removal and noise reduction techniques in this study. These techniques are applied
on the spectral data of two different dust populations, one on the Earth’s Moon
and the second emerging from the south pole of Saturn’s moon Enceladus. First,
the dust population on Earth’s Moon is constrained by obtaining an upper limit on
the number density of dust particles at the Moon’s surface. Lunar dust, due to its
abrasive properties, poses a risk to astronomical observations and the dust densities
limits obtained in this analysis will help plan safer and more reliable future missions
to the Moon. These constraints on dust population in the terminator region of the
Moon can also be used to constrain the Moon’s near-surface environment. Next,
we study the properties of ice-grain particles in Enceladus’s south polar plume by
deriving typical launch velocities and size distribution parameters for the particles
present in the plume. These plume particle properties provide new insights into
vent dynamics, suggesting that particle-particle interactions are more relevant than
previously expected by established models. We also develop a new Machine Learning
algorithm that allows robust constraints on particle size distribution parameters to
be extracted from noisy spectral data. These tools reveal that the plume’s particle
properties can vary in complex ways over multiple timescales. These findings should
constrain the physical processes underlying Enceladus’ plume particle dynamics.