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Tova R. Holmes

Publications and source records attributed to Tova R. Holmes.

2 recordsLinked to original sources

MAIA: A new detector concept for a 10 TeV muon collider

Muon colliders offer a compelling opportunity to explore the TeV scale and conduct precision tests of the Standard Model, all within a relatively compact geographical footprint. This paper introduces a new detector concept, MAIA (Muon Accelerator Instrumented Apparatus), optimized for $\sqrt{s}=10$ TeV $μ^+ μ^-$ collisions. The detector features an all-silicon tracker immersed in a 5T solenoid field. High-granularity silicon-tungsten and iron-scintillator calorimeters surrounding the solenoid capture high-energy electronic and hadronic showers, respectively, and support particle-flow reconstruction. The outermost subsystem comprises an air-gap muon spectrometer, which contributes to muon identification. The performance of the MAIA detector is evaluated in terms of differential particle reconstruction efficiencies and resolutions. Beam-induced background and incoherent pair production simulations are overlaid to single particle gun samples to assess detector reconstruction capabilities under realistic experimental conditions. Even in the presence of backgrounds, reconstruction efficiencies exceed approximately 95\% for energetic tracks, photons, and charged pions in the central region of the detector. This paper outlines promising avenues for future work, including forward region optimization, opportunities for enhanced flavor tagging and boosted object reconstruction, and technological developments needed to achieve the desired detector performance.

physics.ins-det↗

Faculty Orientations Shape Adoption of AI in Research and Teaching

Despite the widespread availability of large language models (LLMs) in higher education, instructors vary substantially in their adoption and use of these tools, and the reasons for this variation remain poorly understood. A mixed-methods survey of 90 STEM faculty in the Research Corporation for Science Advancement (RCSA) Cottrell community examined relationships between AI use, attitudes, institutional context, and instructional practice. Exploratory factor analysis identified a coherent construct, \textit{AI pedagogical orientation}, that strongly predicted self-reported AI use across research, teaching, and other professional activities. Qualitative analysis indicated that this construct reflected differing views about the role AI should play in disciplinary thinking, learning, and expertise development, rather than simply positive or negative attitudes toward AI. Institutional initiatives, demographic variables, and information sources showed comparatively weak associations with AI use. The results suggest that existing technology-adoption models may not fully explain adoption in contexts where technologies interact directly with disciplinary reasoning and knowledge production.

physics.ed-ph↗