2024 Project Description

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Deciphering the Mysteries of Protostars: A Study of Outflows and Jets Using JWST Data
This project can be carried out remotely.

Keywords:

Supervisors

Mayank Narang, Nagayoshi Ohashi
Find out more about supervisors on ASIAA website

Task Description and Goals

Project Description:

This project delves into the captivating world of protostars the nascent stars from which our sun was born. Utilizing data from NASA's James Webb Space Telescope (JWST), we will explore the fascinating process of star formation, specifically focusing on the outflows and jets emanating from protostars .

Objectives:
To analyze the near-infrared data collected by JWST to understand the physical and chemical properties of protostars.
To investigate the molecular composition of these outflows and jets, as they provide insights into the star's internal state and the processes that shape its structure. To compare the behavior of outflows from different stages of star formation to understand the evolution of protostars.
Activities
Data Analysis:
Students will analyze the spectra of protostars from the JWST's Mid-Infrared Instrument (MIRI) to identify key features such as Hydrogen and Carbon Monoxide emission lines, which indicate the presence of molecular outflows.
Image Interpretation: Using the high-resolution imagery from JWST, students will examine the morphology and kinematics of outflows and jets, which can reveal important information about the protostar's environment and growth.
Outcomes
By the completion of this project, students will have gained valuable hands-on experience in astronomical research, contributing to the body of knowledge regarding the universe. They will also have honed their analytical skills, critical thinking, and the ability to communicate complex scientific concepts effectively.
It would be possible for students to continue to work on the project even after the summer school program is completed.

Required Background

Basic understanding of Python or any other high-level language.
Familiarity with statistical analysis