The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. http://repository.arizona.edu:80 2026-06-13T03:42:46Z 2026-06-13T03:42:46Z Foote, Hayden http://hdl.handle.net/10150/680934 2026-06-12T01:12:47Z 2026-01-01T00:00:00Z

New Dynamical Probes of Dark Matter Physics in the Era of Precision Astrometry Foote, Hayden Multiple lines of astrophysical evidence support that roughly 80% of the Universe’s mass is composed of invisible dark matter (DM). However, the nature of the DM particle is still unknown. Efforts to directly detect DM particles with Earth-based experiments rely on direction from local astrophysical probes that can predict the composition and distribution of DM in our Galaxy and, in turn, our Solar System. The era of Gaia precision astrometry has opened new pathways for studying DM in the Milky Way (MW) by unveiling the motions of stars and satellite galaxies orbiting within the gravitational potential of the MW’s DM halo. In this dissertation, I develop three novel theoretical frameworks for utilizing the observed dynamics of the MW’s stellar halo and satellite galaxies to constrain the nature of DM. I begin by introducing the first known collision between an ultrafaint dwarf satellite of the MW, Segue 2, and a stellar stream, Cetus-Palca. This interaction is a promising test bed for modeling the effects of the MW's DM substructure on stellar streams. Additionally, Segue 2’s impact on Cetus-Palca offers a novel method for understanding the structure of the smallest known DM halos. Second, I construct and discuss results from a suite of windtunnel-style N-body simulations of the interaction between the MW and its largest satellite, the LMC, assuming two DM particle candidates, cold and fuzzy DM. I demonstrate that observations of the LMC’s wake in the MW’s stellar halo can be used to confirm the existence of DM and discriminate between cold and fuzzy DM models. Finally, I present a framework for quantifying how the MW’s two largest satellites, the LMC and SMC, shape each other’s DM halos during their ~6 Gyr interaction history. Dynamical studies of the LMC-SMC system must take their time-dependent, asymmetric DM distributions into account. The perturbations to both galaxies’ halos bear similarities to those predicted and observed in the LMC-MW interaction, suggesting the LMC-SMC binary can be used as a powerful DM laboratory. As current and upcoming surveys continue to increase the availability of kinematic data for the MW system, the frameworks I establish in this dissertation offer a promising path forward for understanding the local composition and distribution of DM.

2026-01-01T00:00:00Z Schwartz, David http://hdl.handle.net/10150/680933 2026-06-12T01:12:38Z 2026-01-01T00:00:00Z

Towards Stable Adversarial Training with Stochastic Attention Schwartz, David A new attack surface emerges as neural networks increasingly permeate safety critical applications (e.g., autonomous vehicle control, autonomous surgery, and medical diagnostics). Therefore, due to the potentially life-threatening consequences of successful attacks at these networks' interfaces, the development of principles and methods that robustify vulnerable models is a major interest of the ML community. One factor stymieing these endeavors is the dataset-dependent trade-off typically observed between neural network performance on benign and adversarially altered data. Consequently, there is no clear consensus on how the optimization algorithms and network architectures should navigate this tradeoff. Further exacerbating the dilemma, defending ML models typically increase the overall computational burden at training and/or testing time. As a result, which network architectures and learning algorithms are optimal depends on the user's budgets and constraints on computational power, energy consumption, and time. Hence, light-weight defense techniques that do not prohibitively inflate training time, model footprint, and evaluation time are highly desirable. To that end, this thesis proposes two novel defense techniques that have been empirically demonstrated to improve adversarial accuracy without significant degradation in benign accuracy. In the first method, robustness is enhanced by adding to the loss a regularization term that directly penalizes differences in latent representations evoked throughout a network by adversarial perturbation of the input. The novelty of the second approach is an alteration of the neural network architecture itself. Specifically, to form the algorithm dubbed Data Dependent Stochastic Resonance (DDSR), each hidden attentional layer is augmented with a mechanism that integrates information from earlier layers in a manner reminiscent of Resnet's residual connections, controlling the locations and intensity with which random noise is added to the latent neural representations and the original attentional weights as a function of the earlier layers' activations. This dissertation outlines results from prior work before evaluating some relevant defense methodologies and state-of-the-art techniques to assess the true impacts of the novel ones. These experiments are designed to inform an understanding of adversarial defenses' limits, and their ability to defend against previously unseen attacks, where the adversary holds the adaptive advantage. The ensuing results include a demonstration that despite approaching or matching similarly constrained state-of-the-art (SOTA) defenses' robust accuracy, DDSR retains or recovers benign accuracy at levels matching or exceeding those of the standard (i.e., undefended) instance. With similar budgets for computer hardware or time, combining the defense techniques documented here with present SOTA training techniques, including immense quantities of additional data (i.e., samples not included in the original benchmark datasets) may find evaluations competitive with top results on the RobustBench leaderboard. The impacts of these outcomes are two-fold: firstly by offering engineers deploying neural networks a relatively inexpensive method to improve adversarial robustness without noticeably sacrificing their original objective, and secondly, by elucidating the extent to which information learned to defend against one attack transfers to others, we clarify what computational budget is required to develop acceptable robustness.

2026-01-01T00:00:00Z Escamilla, Aaron http://hdl.handle.net/10150/680932 2026-06-12T01:12:31Z 2026-01-01T00:00:00Z

AI Minimalism: Necessity-First Security Design for Intelligent Systems Escamilla, Aaron The rapid integration of artificial intelligence into cybersecurity operations has outpaced the ability of existing governance frameworks to ensure secure, accountable deployment. While numerous AI security frameworks exist, including those from NIST, ISO, ENISA, and the European Union, they exhibit thematic convergence without operational convergence: frameworks increasingly agree on the categories of risk that matter but diverge on how organizations should translate governance intent into enforceable system design. This thesis argues that AI security failures stem less from insufficient capability and more from excessive, unjustified complexity coupled with weak policy-to-implementation translation, a condition termed policy drift. This thesis introduces AI Minimalism, a Necessity-First, human-centric framework for optimizing AI use in cybersecurity systems. A comparative analysis of ten major global AI security frameworks, evaluated against a four-question rubric assessing necessity, minimalism, human oversight, and operationalization, demonstrates that no existing framework systematically addresses necessity-based design, architectural minimalism, or governance-to-audit traceability. The AI Minimalism framework addresses these gaps through four integrated components: (1) Necessity-First security design, which requires every AI component to justify its inclusion before deployment; (2) human-centric enforcement through risk-tiered Challenge Gates that calibrate oversight to decision criticality; (3) Governance as Code, which translates external framework requirements into structured, machine-readable, version-controlled artifacts; and (4) Policy as Code, which renders internal governance executable, auditable, and subject to continuous drift detection. Feasibility is demonstrated through an illustrative walkthrough in an enterprise-style environment, tracing a complete policy lifecycle from external framework update through governance artifact creation, enforcement logic deployment, and audit evidence generation. The findings suggest that secure AI is achieved not through greater capability or additional complexity, but through justified, minimal, and accountable system design.

2026-01-01T00:00:00Z Gutierrez, Carolina Victoria http://hdl.handle.net/10150/680931 2026-06-12T01:12:25Z 2026-01-01T00:00:00Z

Game Bird Microbiomes as Biomarkers of Host Traits and Health Gutierrez, Carolina Victoria Game birds play an important role in North American economics, particularly in the United States through revenue raised from hunting, restoration, and ecological tourism. These birds are also often iconic organisms with cultural significance on national and regional scales. Moreover, many game bird species are or have been listed as of conservation concern. Wood ducks, an iconic waterfowl species with spectacular plumage, and Northern bobwhites, an endearing upland quail favored by ranchers, are two such game bird species, recovering through targeted conservation efforts.Although these conservation efforts have produced important data that improves our understanding of the ecological constraints affecting birds and populations, there is one aspect of their fitness that has been overlooked, their microbiomes. Microbiomes consist of complex microscopic ecosystems of microorganisms often found on or within host organisms, that perform essential functions and support and influence host health. Determining avian microbiome composition and understanding the various factors that influence these, can therefore, yield important insights into effective avian conservation management. Leveraging samples from ongoing health monitoring of wood ducks and population monitoring of Northern bobwhites, this thesis implements high-throughput 16S rRNA gene sequencing to characterize the bacterial composition of microbiomes from different body sites, and determine associations between diversity and composition with host traits, environmental conditions, and health metrics. Wood ducks were found to have microbiomes significantly influenced by several host traits, variations in environmental conditions, and pathogen presence, whereas Northern bobwhites generally had more uniform microbiomes when comparing across host traits and environmental factors. Notably, cloacal microbiome composition was found to be significantly different between the two species, with many of the differentially abundant taxa having pathogenic potential. Importantly, this study establishes baseline microbiome sequence data for wood ducks and Northern bobwhites, with analyses suggesting actionable insights for conservation management, as well as future research considerations.

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