Discovery and Simulation of Solar System Bodies in the Age of Big Data and Artificial Intelligence
Creators: name: Napier, Kevin
Publisher: name: My University
Year: 2023
Subjects: subject: Solar System
subject: Discovery and Simulation of Solar System Bodies in the Age of Big Data and Artificial Intelligence
subject: Minor Planets
subject: Artificial Intelligence
subject: Numerical simulation of gravitational systems
subject: Astronomy
subject: Physics
subject: Science (General)
subject: Science

description: As science enters an era defined by big data, high-performance computing, and artificial intelligence (AI), we have an opportunity to fundamentally change the way we study our solar system. In this thesis I leverage state-of-the-art computing technology and AI techniques to do analyses that would have been infeasible only a few years ago. The text is split into two parts, corresponding to dynamics and discovery. In Part I, I investigate problems in solar system dynamics by combining pure analytic theory with highly detailed simulations. First I examine the claim that the orbits of the so-called extreme Trans-Neptunian Objects (ETNOs) are being aligned by the gravitational influence of the hypothesized Planet Nine. By carefully simulating the three most productive Kuiper Belt surveys of the past decade, I show that when the surveys' observational biases are fully accounted for, their ETNO detections are consistent with being drawn from an isotropic population, meaning Planet Nine is not necessary to explain the apparent clustering. Next, inspired by the recent detections of the first two interstellar objects 'Oumuamua and Borisov, I investigate the capture of interstellar objects by our solar system. Using a suite of numerical simulations, I calculate the capture cross section for interstellar objects as a function of hyperbolic excess velocity, as well as the characteristic dynamical lifetime of captured objects. Finally in Chapter 6, motivated by the continued absence of any known dynamically stable Earth Trojans, I propose a collisional mechanism by which such objects may have been destabilized, and show that an initially-stable population of primordial Earth Trojans would have been severely disrupted by asteroid impacts on Earth. In Part II of this thesis I develop new techniques for discovering faint solar system bodies, beginning with a search for Kuiper Belt objects in data from the DECam Ecliptic Exploration Project (DEEP). By combining a shift-and-stack technique with convolutional neural networks, I reach an average depth of m ~ 26.2, accumulating ~ 2300 single-epoch detections. The main scientific result of this work is a measurement of the absolute magnitude distribution of the Cold Classical Kuiper Belt. Our data is well-fit by an exponentially-tapered power law, which is the functional form predicted by streaming instability simulations of planetesimal formation. In Chapter 8 I adapt the techniques from the DEEP search to do a proof-of-concept shift-and-stack search for Jupiter Trojans. The search yielded more than 100 new Jupiter Trojans, with an extremely low false positive rate prior to human inspection. In Chapter 9 I present a search for a flyby target for NASA's New Horizons spacecraft. Using data from four DECam fields, I used a novel combination of shift-and-stack, convolutional neural networks, and orbit linking to reject false positive detections. While the search did not turn up a flyby target, it did uncover some candidate objects that may be observable by the spacecraft. The technique is extremely promising, and warrants further study. Finally I introduce a novel approach to the minor planet detection problem, called HelioStack. By shifting perspective from the topocentric frame to the heliocentric frame, and then choosing an appropriate orbit parameterization, the algorithm can drastically simplify the description of the apparent motion of bodies on Keplerian orbits. It will be critical for enabling sub-threshold minor planet detection in future astronomical surveys such as LSST.