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Description
Information about the elemental composition of a planetary surface can be determined using nuclear instrumentation such as gamma-ray and neutron spectrometers (GRNS). High-energy Galactic Cosmic Rays (GCRs) resulting from cosmic super novae isotropically bombard the surfaces of planetary bodies in space. When GCRs interact with a body’s surface, they can

Information about the elemental composition of a planetary surface can be determined using nuclear instrumentation such as gamma-ray and neutron spectrometers (GRNS). High-energy Galactic Cosmic Rays (GCRs) resulting from cosmic super novae isotropically bombard the surfaces of planetary bodies in space. When GCRs interact with a body’s surface, they can liberate neutrons in a process called spallation, resulting in neutrons and gamma rays being emitted from the planet’s surface; how GCRs and source particles (i.e. active neutron generators) interact with nearby nuclei defines the nuclear environment. In this work I describe the development of nuclear detection systems and techniques for future orbital and landed missions, as well as the implications of nuclear environments on a non-silicate (icy) planetary body. This work aids in the development of future NASA and international missions by presenting many of the capabilities and limitations of nuclear detection systems for a variety of planetary bodies (Earth, the Moon, metallic asteroids, icy moons). From bench top experiments to theoretical simulations, from geochemical hypotheses to instrument calibrations—nuclear planetary science is a challenging and rapidly expanding multidisciplinary field. In this work (1) I describe ground-truth verification of the neutron die-away method using a new type of elpasolite (Cs2YLiCl6:Ce) scintillator, (2) I explore the potential use of temporal neutron measurements on the surface of Titan through Monte-Carlo simulation models, and (3) I report on the experimental spatial efficiency and calibration details of the miniature neutron spectrometer (Mini-NS) on board the NASA LunaH-Map mission. This work presents a subset of planetary nuclear science and its many challenges in humanity's ongoing effort to explore strange new worlds.
ContributorsHeffern, Lena Elizabeth (Author) / Hardgrove, Craig (Thesis advisor) / Elkins-Tanton, Linda (Committee member) / Parsons, Ann (Committee member) / Garvie, Laurence (Committee member) / Holbert, Keith (Committee member) / Lyons, James (Committee member) / Arizona State University (Publisher)
Created2022
Description

Lightning in the atmosphere of Venus is either ubiquitous, rare, or non-existent, depending on how one interprets diverse observations. Quantifying if, when, or where lightning occurs would provide novel information about Venus’s atmospheric dynamics and chemistry. Lightning is also a potential risk to future missions, which could float in the

Lightning in the atmosphere of Venus is either ubiquitous, rare, or non-existent, depending on how one interprets diverse observations. Quantifying if, when, or where lightning occurs would provide novel information about Venus’s atmospheric dynamics and chemistry. Lightning is also a potential risk to future missions, which could float in the cloud layers (~50–70 km above the surface) for up to an Earth-year. For decades, spacecraft and ground-based telescopes have searched for lightning at Venus, using many instruments including magnetometers, radios, and optical cameras. Two surveys (from the Akatsuki orbiter and the 61-inch telescope on Mt. Bigelow, Arizona) observed several optical flashes that are often attributed to lightning. We expect that lightning at Venus is bright near 777 nm (the unresolved triplet emission lines of excited atomic oxygen) due to the high abundance of oxygen as carbon dioxide. However, meteor fireballs at Venus are probably bright at the same wavelength for the same reason. Here we derive power laws that quantify the rate and brightness of optical flashes from meteor fireballs at Venus. We calculated that meteor fireballs are statistically likely to cause bright optical flashes at rates that are consistent with published observations. Small meteors burn up at altitudes of ~100 km, roughly twice as high above the surface as the clouds. Therefore, we conclude that there is no concrete evidence that lightning strikes would be a hazard to missions that pass through or dwell within the clouds of Venus.

ContributorsBlaske, Claire (Author) / O'Rourke, Joseph (Thesis director) / Desch, Steve (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor)
Created2023-05
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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

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