arXiv subjects

Morgan Macleod

Publications and source records attributed to Morgan Macleod.

5 recordsLinked to original sources

arXiv2026

Semi-analytical Light Curve Model for Transients Preceding Binary Mergers. I: Supernova Precursor Emission from Compact Object Companions

A binary undergoing dynamically unstable mass transfer could rapidly shrink its orbit and lead to a merger, with the whole process often observable in human timescales. We construct a semi-analytical light curve model of binary systems composed of a star and a compact object accretor, that would display long-rising accretion-powered emission prior to a final merger-driven explosion. We apply the model to the precursors of interacting supernovae (SNe) that display long-rising light curves of years, SN 2023zkd, 2023fyq and 2021qqp, demonstrating the model's capability of inferring the mass-transfer history and constraining the progenitor binary system. The model and the parameter inference framework, encapsulated in a publicly-released script, can be applied to existing long-rising precursors of SNe as well as many SN precursors to be discovered by surveys like those from the Vera C. Rubin Observatory.

astro-ph.HE↗

arXiv2026

From Novelty to Normalisation: Tracking Changing Perceptions of AI in Higher Education, 2024-2026

The rapid integration of generative artificial intelligence (AI) has reshaped the landscape of higher education. Students have embraced tools such as ChatGPT with striking speed, while teaching staff and institutions have responded with greater caution. Existing research on AI perceptions has mainly been cross-sectional, providing single-point snapshots that view attitudes as stable rather than evolving. This paper presents a longitudinal study of AI perceptions in higher education, tracking undergraduates, doctoral researchers, teaching staff and non-teaching staff at Ulster University across three survey waves between 2024 and 2026 (n=1,665). A quantitative survey design measured familiarity, reported use and perceived risk; results show that students rapidly normalised AI use over the period, moving from tentative experimentation to routine engagement, while staff expressed persistent concerns about academic integrity, assessment design, and critical thinking. Doctoral and non-teaching staff occupied intermediate positions, reflecting both pragmatic adoption and institutional caution. The student-staff gap widened as institutional policy struggled to keep pace with actual practice. By tracking these shifts directly rather than reconstructing them from disconnected studies, the paper moves beyond descriptive accounts of AI attitudes and demonstrates the importance of capturing perceptions in real time. The findings carry significant implications for adaptive institutional policy, AI literacy initiatives, and targeted staff training.

cs.CY↗

arXiv2025

Methodological Variation in Studying Staff and Student Perceptions of AI

In this paper, we compare methodological approaches for comparing student and staff perceptions, and ask: how much do these measures vary across different approaches? We focus on the case of AI perceptions, which are generally assessed via a single quantitative or qualitative measure, or with a mixed methods approach that compares two distinct data sources - e.g. a quantitative questionnaire with qualitative comments. To compare different approaches, we collect two forms of qualitative data: standalone comments and structured focus groups. We conduct two analyses for each data source: with a sentiment and stance analysis, we measure overall negativity/positivity of the comments and focus group conversations, respectively. Meanwhile, word clouds from the comments and a thematic analysis of the focus groups provide further detail on the content of this qualitative data - particularly the thematic analysis, which includes both similarities and differences between students and staff. We show that different analyses can produce different results - for a single data source. This variation stems from the construct being evaluated - an overall measure of positivity/negativity can produce a different picture from more detailed content-based analyses. We discuss the implications of this variation for institutional contexts, and for the comparisons from previous studies.

cs.HC↗

arXiv2025

Revealing a main-sequence star that consumed a planet with JWST

The subluminous red nova (SLRN) ZTF SLRN-2020 is the most compelling direct detection of a planet being consumed by its host star, a scenario known as a planetary engulfment event. We present JWST spectroscopy of ZTF SLRN-2020 taken +830 d after its optical emission peak using the NIRSpec fixed-slit $3-5$ $\mu$m high-resolution grating and the MIRI $5-12$ $\mu$m low-resolution spectrometer. NIRSpec reveals the $^{12}$CO fundamental band ($\nu=1-0$) in emission at $\sim4.7$ $\mu$m, Brackett-$\alpha$ emission, and the potential detection of PH$_3$ in emission at $\sim4.3$ $\mu$m. The JWST spectra are consistent with the claim that ZTF SLRN-2020 arose from a planetary engulfment event. We utilize DUSTY to model the late-time $\sim1-12$ $\mu$m spectral energy distribution (SED) of ZTF SLRN-2020, where the best-fit parameters indicate the presence of warm, $720^{+80}_{-50}$ K, circumstellar dust with a total dust mass of Log$\left(\frac{M_\mathrm{d}}{\mathrm{M}_\odot}\right)=-10.61^{+0.08}_{-0.16}$ M$_\odot$. We also fit a DUSTY model to archival photometry taken +320 d after peak that suggested the presence of a cooler, T$_\mathrm{d}=280^{+450}_{-20}$ K, and more massive, Log$\left(\frac{M_\mathrm{d}}{\mathrm{M}_\odot}\right)=-5.89^{+0.29}_{-3.21}$, circumstellar dust component. Assuming the cool component originates from the ZTF SLRN-2020 ejecta, we interpret the warm component as fallback from the ejecta. From the late-time SED model we measure a luminosity of L$_* = 0.29^{+0.03}_{-0.06}$ L$_\odot$ for the remnant host star, which is consistent with a $\sim0.7$ M$_\odot$ K-type star that should not yet have evolved off the main sequence. If ZTF SLRN-2020 was not triggered by stellar evolution, we suggest that the planetary engulfment was due to orbital decay from tidal interactions between the planet and the host star.

astro-ph.SR↗

arXiv2024

AI Across Borders: Exploring Perceptions and Interactions in Higher Education

This study investigates students' perceptions of Generative Artificial Intelligence (GenAI), with a focus on Higher Education institutions in Northern Ireland and India. We collect quantitative Likert ratings and qualitative comments from 1211 students on their awareness and perceptions of AI and investigate variations in attitudes toward AI across institutions and subject areas, as well as interactions between these variables with demographic variables (focusing on gender). We found the following: (a) while perceptions varied across institutions, responses for Computer Sciences students were similar, both in terms of topics and degree of positivity; and (b) after controlling for institution and subject area, we observed no effect of gender. These results are consistent with previous studies, which find that students' perceptions are predicted by prior experience; crucially, however, the results of this study contribute to the literature by identifying important interactions between key factors that can influence experience, revealing a more nuanced picture of students' perceptions and the role of experience. We consider the implications of these relations, and further considerations for the role of experience.

cs.CY↗