The Astro Online Seminar series begins again with Sharmalee Kapse exploring multiple stellar populations in our neighbourhood
Overview: Observational studies of star clusters are important to constrain stellar evolution theory and the star formation histories of their host galaxies. Traditionally, star clusters were believed to represent "simple" stellar populations (SSPs), with all member stars reflecting a similar age and a unimodal chemical composition. However, detection of star-to-star abundance spreads in light elements (C, N, O, Na, Mg, Al) in young (~ 1--3 Gyr-old) clusters has revolutionized this field. In the last decade, many young and intermediate-age (~ 3--6 Gyr-old) clusters have been found to display deviations from the SSP concept, particularly in terms of their ages, chemical compositions and rotation rates. I will discuss the simple and complex stellar populations in star clusters in the Magellanic Clouds. Using Hubble Space Telescope imaging data we investigated the stellar evolutionary stages of young and intermediate-age Magellanic Cloud clusters to understand properties such as their helium and nitrogen abundance variations. Studies of young star clusters in nearby galaxies can provide clues to the star formation history of our own galaxy, the Milky Way.
About the speaker: Shalmalee Kapse is a PhD student studying the multiple stellar populations in the Magellanic Clouds at Macquarie University.
The Magellanic Clouds are Milky Way's nearest dwarf galaxies and they have many dense star clusters which vary in mass, age, chemical compositions. Shalmalee’s particular interests are young and massive star clusters in these galaxies. She explains, “Almost all the old clusters are categorized under simple stellar population, meaning each member of these clusters is coeval having the same mass and same chemical composition. However, recent observations from the space telescopes, such as Hubble Space Telescope have shown that some old and a few new clusters exhibit a different picture.” It is this differentiation which interests Shalmalee, which she explains as “a star-to-star variation in chemical compositions in some of the clusters; phenomenon called multiple stellar populations.”
Shalmalee is trained in Python (NumPy, Matplotlib, Pandas) and R. She uses scipy and scikit-learn for the statistical data analysis part of her projects. Furthermore, she applies machine learning techniques to the vast data collected from the space- and ground-based telescopes.