Senior Project Advisor

Kevin Covey

Document Type

Project

Publication Date

Spring 2021

Keywords

Star formation, young stars, machine learning

Abstract

Stellar ages can act as a marker of birth cluster membership for young stellar objects (YSOs), which allows for an improved understanding of the history of star formation in the solar neighborhood. However, the ages of YSOs have historically been difficult to predict on a large scale. Here, we develop a system of convolution neural network models to differentiate between YSOs and their more-evolved counterparts and predict YSO ages using Gaia and 2MASS photometry. The full model and resulting catalog recovers the properties of well-studied young stellar populations to a distance of five kiloparsecs, with significantly higher sensitivity within one kiloparsec, while also identifying new YSO candidate stars. We then explore the resulting catalog's implications for solar neighborhood star formation, and identify several large-scale structures, including two interesting ring or bubble-shaped groupings of young stars which may suggest radially triggered star forming events. Our results support the existence of an inclined Gould's Belt of local star formation, which may coincide with the Local Bubble. In addition, we also identify 26 high velocity 'runaway' stars from the Orion Nebula Cluster and characterize their likely origins.

Department

Physics/Astronomy

Subjects - Topical (LCSH)

Stars--Formation; Neural networks (Computer science); Stars--Populations

Subjects - Names (LCNAF)

Orion Nebula

Genre/Form

essays

Type

Text

Rights

Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.

Language

English

Format

application/pdf

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