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- All Subjects: Planetary Science
- Creators: Ben Amor, Heni
Description
Affect is a domain of psychology that includes attitudes, emotions, interests, and values. My own affect influenced the choice of topics for my dissertation. After examining asteroid interiors and the Moon’s thermal evolution, I discuss the role of affect in online science education. I begin with asteroids, which are collections of smaller objects held together by gravity and possibly cohesion. These “rubble-pile” objects may experience the Brazil Nut Effect (BNE). When a collection of particles of similar densities, but of different sizes, is shaken, smaller particles will move parallel to the local gravity vector while larger objects will do the opposite. Thus, when asteroids are shaken by impacts, they may experience the BNE as possibly evidenced by large boulders seen on their surfaces. I found while the BNE is plausible on asteroids, it is confined to only the outer layers. The Moon, which formed with a Lunar Magma Ocean (LMO), is the next topic of this work. The LMO is due to the Moon forming rapidly after a giant impact between the proto-Earth and another planetary body. The first 80% of the LMO solidified rapidly at which point a floatation crust formed and slowed solidification of the remaining LMO. Impact bombardment during this cooling process, while an important component, has not been studied in detail. Impacts considered here are from debris generated during the formation of the Moon. I developed a thermal model that incorporates impacts and find that impacts may have either expedited or delayed LMO solidification. Finally, I return to affect to consider the differences in attitudes towards science between students enrolled in fully-online degree programs and those enrolled in traditional, in-person degree programs. I analyzed pre- and post-course survey data from the online astrobiology course Habitable Worlds. Unlike their traditional program counterparts, students enrolled in online programs started the course with better attitudes towards science and also further changed towards more positive attitudes during the course. Along with important conclusions in three research fields, this work aims to demonstrate the importance of affect in both scientific research and science education.
ContributorsDingatantrige Perera, Jude Viranga (Author) / Asphaug, Erik (Thesis advisor) / Semken, Steven (Thesis advisor) / Anbar, Ariel (Committee member) / Elkins-Tanton, Linda T. (Committee member) / Robinson, Mark (Committee member) / Arizona State University (Publisher)
Created2017
Description
There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance our understanding of the past, present, and future of the solar system and universe. As more missions come online and the volume of data increases, it becomes more difficult for scientists to analyze these complex data at the desired pace. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and prioritize the most promising, novel, or relevant observations for scientific analysis. Machine learning methods can serve this need in a variety of ways: by uncovering patterns or features of interest in large, complex datasets that are difficult for humans to analyze; by inspiring new hypotheses based on structure and patterns revealed in data; or by automating tedious or time-consuming tasks. In this dissertation, I present machine learning solutions to enhance the tactical planning process for the Mars Science Laboratory Curiosity rover and future tactically-planned missions, as well as the science analysis process for archived and ongoing orbital imaging investigations such as the High Resolution Imaging Science Experiment (HiRISE) at Mars. These include detecting novel geology in multispectral images and active nuclear spectroscopy data, analyzing the intrinsic variability in active nuclear spectroscopy data with respect to elemental geochemistry, automating tedious image review processes, and monitoring changes in surface features such as impact craters in orbital remote sensing images. Collectively, this dissertation shows how machine learning can be a powerful tool for facilitating scientific discovery during active exploration missions and in retrospective analysis of archived data.
ContributorsKerner, Hannah Rae (Author) / Bell, James F. (Thesis advisor) / Ben Amor, Heni (Thesis advisor) / Wagstaff, Kiri L (Committee member) / Hardgrove, Craig J (Committee member) / Shirzaei, Manoochehr (Committee member) / Arizona State University (Publisher)
Created2019