Chasing Metamorphic Supernovae with Zwicky Transient Facility, SEDM-KP, and AI

Year: 2025 DOI: 10.7907/f5wm-ay36
Modern time-domain astronomy has entered a data-rich era. Propelled by wide-field, high-cadence surveys like the Zwicky Transient Facility (ZTF) have vastly expanded our understanding of supernova (SN) diversity. However, the surge in discoveries has led to a classification bottleneck, particularly for spectroscopic follow-up, hindering the timely identification of rare or unusual transients. This thesis focuses on a class of unusually long-lived SNe with bumpy light curves, and also addresses the broader classification challenge through instrumentation and the application of artificial intelligence.

Two rare SN classes are examined in depth through systematic samples: (i) SNe Ia-CSM, which initially have SNe Ia-like spectra but later transform into Type IIn-like SNe strongly interacting with circumstellar material (CSM), challenging our understanding of their progenitor systems; and (ii) double-peaked stripped-envelope supernovae (SESNe), where multiple light curve peaks suggest contributions from diverse energy sources including double-nickel distribution, CSM interaction, or magnetar engines. I derive constraints on the observed rates of SNe Ia-CSM with the systematic sample, and identify spectroscopic features that can differentiate between the strongly-interacting spectra of SNe Ia-CSM from SNe IIn. I discuss the diversity of double-peaked SESN light curves in the context of the plethora of suggested powering mechanisms and derive light curve properties that can help narrow down the possibilities.

To enable more effective discovery and classification of such events, this thesis also presents instrumental and computational advances. I detail the commissioning of a new low-resolution robotic spectrograph, SEDM-KP, on the Kitt Peak 84-inch telescope, designed to extend spectroscopic classification to fainter transients. Additionally, I introduce a deep-learning-based tool, CCSNscore, which achieves high accuracy in automated core-collapse supernova classification from low-resolution spectra, significantly reducing human workload and latency in reporting.

Together, these contributions advance our ability to identify, classify, and study the growing zoo of transient phenomena and lay the groundwork for managing the deluge of discoveries anticipated in the Rubin Observatory era.