2026 Project Description

Exploring the origin of accretion outbursts in star formation

Supervisors

Indrani Das
Find out more about supervisors on ASIAA website

Task Description and Goals

Stellar mass is the most fundamental property of a star, governing its physical characteristics and evolutionary path, yet how low-mass stars acquire their mass remains a question. Observations show that protostars are significantly less luminous than predicted by models assuming steady mass accretion, a discrepancy known as the luminosity problem. A leading explanation is accretion variability, in which protostars gain mass through short-lived, high-mass accretion outbursts separated by longer quiescent phases. FU Orionis (FUors)–type objects, which exhibits a drastic increment in luminosity over a time period of decades or longer, are prime examples of such accretion outburst events. During these outbursts, mass accretion rates rise by orders of magnitude, allowing substantial stellar mass growth over short timescales. As a result, FUor-like accretion outbursts are considered to play a critical role in the mass assembly of low-mass stars.

In this project, we aim to generate synthetic FUor light curves using radiative transfer calculations based on the simulation data of episodic accretion-bursts. The first step will be to convert the model data into the radiation intensity. Then the second step will be to process the model intensities with the corresponding filter capacity to generate synthetic light curves in different photometric bands. The synthetic FUor light curves, particularly during the rapid rise phase of the accretion burst, can provide key diagnostics of outburst propagation and burst-triggering mechanisms within the inner protoplanetary disk.

Through this project, students will gain knowledge of star and planet formation, protoplanetary disk physics, basic introduction to the observations as well as practical experience with radiative transfer modeling, generating synthetic observations, and Python-based scientific computing.

Required Background

Background knowledge in astrophysics is preferred, but not mandatory. Experience in Linux/Unix systems and programming using Python is desirable. Good English communication skills are required.