| dc.description.abstract | Study CMD of Globular Clusters using Gaia DR3
data and classify them according to their morphology
using Machine Learning
by
Bishnu Kumar Sharma
Submitted to the Graduate Institute of Astronomy
in the partial fulfillment of the
requirement for the degree of Master of Astronomy
Abstract
Globular clusters are the agglomeration of the old and metal-poor stars into a
spherical shape. Since they are one of the oldest stellar objects, their study can
help us understand the physical and chemical structure as well as the evolution of the universe. As the first part of our research, we use Gaia DR3 data
to construct color-magnitude diagrams (CMDs) for the Galactic globular clusters (GCs). We use various information like position, proper motion, and photometry of the individual GC and we extract the data using the VizieR Queries
(astroquery.vizier) to construct CMD. We use different selection criteria to select member stars and to remove outliers. Using our selection criteria, among
150 GCs, we are able to get 57 CMDs with proper morphology. Standardization
of magnitudes to absolute magnitude has been done using distance modulus
and extinction correction from Schlegal, Flinkbeiner, and Davis. Our aim is to
classify the GCs based on CMD morphology such as main-sequence, red-giant,
and horizontal branches using a pure machine learning approach and study the
properties of the individual groups. We use TensorFlow, a framework to build
a neural network along with Keras, a high-level API for building and training
neural networks. We make an ML model for image classification and use Kmeans clustering for making groups of similar images. By superimposing the
CMDs of individual groups, and making them one CMD, we studied the properties like age, distance, metallicity, etc of individual clusters of a group. We find
5 groups having nearly similar ages and metallicities and we consider we get 5
GCs groups as twins.
Keywords: Globular clusters, Color-magnitude diagram, Machine learning
Thesis Supervisor: Chow-Choong Ngeow
Title: Associate Professor | en_US |