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Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Astronomy

Year Degree Awarded

2018

Month Degree Awarded

May

First Advisor

Daniela Calzetti

Second Advisor

Mauro Giavalisco

Third Advisor

Stella Offner

Fourth Advisor

Krista Gile

Subject Categories

External Galaxies

Abstract

Star clusters form the basic building blocks of galaxies. They span a wide range of ages, from a few million years to billions of years, making them exceptional tracers of the star formation histories of their host galaxies. Star formation is the process by which galaxies build up their stellar populations and their visible mass and occurs in a continuous, hierarchical "social" fashion across a large dynamical range, from individual stars up to kiloparsec-scale ensembles of stellar aggregates. It is the formation, evolution, and eventual destruction of these large hierarchical star-forming complexes that provide an essential role in understanding the physical mechanism and dynamical evolution of star formation on sub-galactic scales.

First, using star clusters from local galaxies as part of the LEGUS (Legacy Extragalactic UV Survey) sample, we find that star formation is coherent over scales of a few hundred parsec up to a few kpc depending on the galaxy. In all cases, these hierarchies are short lived and unbound, dissolving in a few tens to a hundred Myr. The recovered correlations between the spatial separations and ages of star clusters contained within these structures are consistent with theoretical expectations of arising from a turbulence-driven ISM. We also find evidence that the maximum size of correlated star formation is driven by galactic shear.

Second, we combine our star cluster catalogs with exquisite molecular gas observations to connect the detailed stellar population information to the natal gas from which it formed. We find that the timescale for star clusters to lose association with their natal clouds is of order a few Myr, with their ages rising rapidly as they become spatially separated from their molecular clouds.

Third, we introduce initial work that employs the use of machine learning as a process to identify star clusters, a quicker and more homogeneous method than traditional visual classification techniques employed for most stellar cluster catalogs.

The work contained in this dissertation represents the first large-scale study of its kind outside of the Local Group to characterize turbulence as the physical driver of correlated star formation and the association timescale of star clusters with their molecular reservoirs, marking a turning point in the effort to link local star forming structures to those that are common at high redshift.

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