A Catalog of Extended Green Objects for ALMAGAL using the GLIMPSE Survey

  • Andrew Oldnall

Student thesis: Master of Science by Research

Abstract

Exploration into the phases of High-Mass Star (HMS) forming regions is essential to developing a comprehensive understanding of galactic evolution. However, evidence suggests that such exploration is limited by the complexity of the surrounding environment, which severely shortens the evolutionary timescale of these regions. This is due to rapid evolution causing HMS to spend less time in each phase for the duration of their life cycle compared to low-mass stars; consequentially making these regions more obscure to observe and catalog. Accordingly, this research project is attempting to catalog images captured of HMS forming regions taken from the Galactic Legacy Infrared Midplane Survey Extraordinaire (GLIMPSE) survey of 1017 sources proposed by the ALMAGAL project, with three aims: first, to detect EGOs using an original automated algorithm, second to cross-validate findings with previous catalogs of Extended Green Objects (EGOs), which are indicators for HMS formation, and third, to catalog potential EGOs and perform analysis on statistically significant sources within these regions. A Python script was created following an EGO detection algorithm titled the Green Fuzzy Finder with the purpose of detecting and cataloging EGOs within the 1017 sources via the ALMAGAL project. The results show that considering a 180 arcsecond$^2$ image, out of 1017 sources, 376 were classified as containing potential EGOs, with a total of 697 objects detected, 288 within the ALMA field of view of 35 arcseconds and 409 outside of this field of view, thus producing a large and comprehensive catalog. It can be concluded that the algorithm created detects possible EGOs within the ALMAGAL dataset. These results are consistent with previous catalogs suggesting the importance that automated algorithms play in detecting EGOs indicating HMS formation. Given the lack of research on this topic, the findings will both help to develop a framework that seeks to explain the phenomena that are EGOs while aiding the ALMAGAL project by detecting statistically significant sources.
Date of Award6 Jan 2025
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorChristopher Conselice (Supervisor) & Gary Fuller (Supervisor)

Keywords

  • Spitzer
  • GLIMPSE
  • ALMAGAL
  • Extended Green Object

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