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- All Subjects: astrobiology
- Creators: Hartnett, Hilairy
Description
The origin of life remains unknowable to current science. Scientists cannot see into the origin of life on Earth, and until humanity discovers life elsewhere in the universe and begin to compare this alien life to Earth, it is likely to be undiscoverable. However, alien life may be so different from life as it is currently known that it may not be recognizable when it is found. Therefore, astrobiology needs a universal theory for life to avoid detection methods being biased towards Earth-based life. This also extends to the instrumentation sent into space, which should be built to detect universal properties of life. Assembly theory, a novel measure of complexity and arguably the only testable agnostic biosignature in current science, is used here to provide precision requirements for mass spectrometry instrumentation on future spaceflight missions with the goal of finding life elsewhere. Universal properties are not only applicable to the origins of life, but also to technologically advanced societies. Predictable patterns are found in today’s industrially based society, such as energy usage as a function of population density. These patterns may serve as the basis for technosignatures that are evidence of advanced extraterrestrial civilizations. Patters found in patent chemistry are explored, as well as predictions of chemical complexity based on assembly theory, to determine how complex chemistry is built by human society and which statistical patterns may be found in extraterrestrial civilizations. Moving beyond astrobiology, science cannot be done in a vacuum but must be communicated and taught to others. Topics such as a universal definition of life, biosignatures, and increasing complexity mean nothing without interest and engagement from others, particularly students. To this end, transformative pedagogical tools are used, particularly sociotransformative constructivism (sTc), to build and teach an Earth Science and Astrobiology curriculum to a classroom of high school incarcerated students. The impact of this class on their science learning and how they personally identify as scientists is studied.
ContributorsMalloy, John (Author) / Walker, Sara (Thesis advisor) / Reano, Darryl (Committee member) / Hartnett, Hilairy (Committee member) / Trembath-Reichert, Elizabeth (Committee member) / Cronin, Leroy (Committee member) / Arizona State University (Publisher)
Created2023
Description
With the ability to observe the atmospheres of terrestrial exoplanets via transit spectroscopy on the near-term horizon, the possibility of atmospheric biosignatures has received considerable attention in astrobiology. While traditionally exoplanet scientists looking for life focused on biologically relevant trace gases such as O2 and CH4, this approach has raised the spectre of false positives. Therefore, to address these shortcomings, a new set of methods is required to provide higher confidence in life detection. One possible approach is measuring the topology of atmospheric chemical reaction networks (CRNs). To investigate and assess this approach, the ability of network-theoretic metrics to distinguish the distance from thermochemical equilibrium in the atmosphere of hot jupiters was tested. After modeling the atmospheres of hot jupiters over a range of initial conditions using the VULCAN modeling package, atmospheric CRNs were constructed from the converged models and their topology measured using the Python package NetworkX. I found that network metrics were able to predict the distance from thermochemical equilibrium better than atmospheric species abundances alone. Building on this success, I modeled 30,000 terrestrial worlds. These models divided into two categories: Anoxic Archean Earth-like planets that varied in terms of CH4 surface flux (modeled as either biotic or abiotic in origin), and modern Earth-like planets with and without a surface flux of CCl2F2 (to represent the presence of industrial civilizations). I constructed atmospheric CRNs from the converged models, and analyzed their topology. I found that network metrics could distinguish between atmospheres with and without the presence of life or technology. In particular, mean degree and average shortest path length consistently performed better at distinguishing between abiotic and biotic Archean-like atmospheres than CH4 abundance.
ContributorsFisher, Theresa Mason (Author) / Walker, Sara I (Thesis advisor) / Hartnett, Hilairy (Committee member) / Line, Michael (Committee member) / Shkolnik, Evgenya (Committee member) / Okie, Jordan (Committee member) / Arizona State University (Publisher)
Created2023