Radiative transfer modeling of outbursts of massive young stellar objects

TL;DR

This paper models outbursts of massive young stellar objects using radiative transfer simulations, revealing different triggering mechanisms and introducing a new time-dependent RT modeling tool for understanding these energetic events.

Contribution

It presents the first time-dependent radiative transfer modeling of MYSO outbursts and develops a novel fitting tool for future studies.

Findings

G323's burst is the most energetic observed for a MYSO.

G358's burst was shorter and weaker, likely caused by accretion of a small object.

Time-dependent RT modeling successfully captures the burst dynamics.

Abstract

Young stellar objects (YSOs) accrete up to half of their material in short periods of enhanced mass accretion. For massive YSOs (MYSOs with more than 8 solar masses), accretion outbursts are of special importance, as they serve as diagnostics in highly obscured regions. Within this work, two outbursting MYSOs within different evolutionary stages, the young source G358.93-0.03 MM1 (G358) and the more evolved one G323.46-0.08 (G323), are investigated, and the major burst parameters are derived. For both sources, follow-up observations with the airborne SOFIA observatory were performed to detect the FIR afterglows. All together, we took three burst-/post-observations in the far infrared. The burst parameters are needed to understand the accretion physics and to conclude on the possible triggering mechanisms behind it. Up to today, G323s burst is the most energetic one ever observed for a MYSO. G358s burst was about two orders of magnitude weaker and shorter (2 months instead of 8 years). We suggest that G358s burst was caused by the accretion of a spiral fragment (or a small planet), where G323 accreted a heavy object (a planet or even a potential companion). To model those sources, we use radiative transfer (RT) simulations (static and time-dependent). G323s accretion burst is the first astrophysical science case, that is modeled with time-dependent RT (TDRT). We incorporate a small TDRT parameter-study and develop a time-depending fitting tool (the TFitter) for future modeling.