Date of Graduation

12-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Space & Planetary Sciences (PhD)

Degree Level

Graduate

Department

Space & Planetary Sciences

Advisor/Mentor

Chevrier, Vincent F.

Committee Member

Roe, Larry A.

Second Committee Member

Oliver, William F. III

Third Committee Member

Chen, Jingyi

Keywords

Astroinformatics; Astronomy data analysis; Astrostatistics techniques; Classical Kuiper Belt objects; Radiative transfer; Trans-Neptunian objects

Abstract

Decoding surface-atmospheric interactions and volatile transport mechanisms on trans-Neptunian objects (TNOs) and Kuiper Belt objects (KBOs) involves an in-depth understanding of physical and thermal properties and spatial distribution of surface constituents – nitrogen (N2), methane (CH4), carbon monoxide (CO), and water (H2O) ices. This thesis implements a combination of radiative scattering models, machine learning techniques, and laboratory experiments to investigate the uncertainties in grain size estimation of ices, the spatial distribution of surface compositions on Pluto, and the thermal properties of volatiles found on TNOs and KBOs. Radiative scattering models (Mie theory and Hapke approximations) were used to compare single scattering albedos of N2, CH4, and H2O ices from their optical constants at near-infrared wavelengths (1 – 5 µm). Based on the results of Chapters 2 and 3, this thesis recommends using the Mie model for unknown spectra of outer solar system bodies in estimating grain sizes of surface ices. When using an approximation for radiative transfer models (RTMs), we recommend using the Hapke slab approximation model over the internal scattering model. In Chapter 4, this thesis utilizes near-infrared (NIR) spectral observations of the LEISA/Ralph instrument onboard NASA’s New Horizons spacecraft. Hyperspectral LEISA data were used to map the geographic distribution of ices on Pluto’s surface by implementing the principal component reduced Gaussian mixture model (PC-GMM), an unsupervised machine learning technique. The distribution of ices reveals a latitudinal pattern with distinct surface compositions of volatiles. The PC-GMM method was able to recognize local-scale variations in surface compositions of geological features. The mapped distribution of surface units and their compositions are consistent with existing literature and help in an improved understanding of the volatile transport mechanism on the dwarf planet. In Chapter 5, we propose a method to estimate thermal conductivity, volumetric heat capacity, thermal diffusivity, and thermal inertia of N2, CH4, and CO ices, and mixtures thereof in a simulated laboratory setting at temperatures of 20 to 60 K – relevant to TNOs and KBOs. A new laboratory experimental facility – named the Outer Solar System Astrophysics Lab (OSSAL) – was built to implement the proposed method. This thesis provides detailed technical specifications of that laboratory with an emphasis on facilitating the design of similar cryogenic facilities in the future. Thus, this research was able to incorporate a set of methods, tools, and techniques for an improved understanding of ices found in the Kuiper Belt and to decipher surface-atmospheric interactions and volatile transport mechanisms on planetary bodies in the outer solar system.

Download

Share

COinS