Early Planet Formation in Embedded Disks (edisk) XIV: Flared Dust Distribution and Viscous Accretion Heating of the Disk Around R CrA IRS 7B-a

Shigehisa Takakuwa,Kazuya Saigo，Yoshihide Yamato,Hsi-Wei Yen

The Astrophysical Journal（2024）SCI 2区SCI 3区

Cited 3|Views49

Abstract

We performed radiative transfer calculations and observing simulations to reproduce the 1.3 mm dust-continuum and C ^18 O (2–1) images in the Class I protostar R CrA IRS7B-a, observed with the ALMA Large Program “Early Planet Formation in Embedded Disks (eDisk).” We found that a dust disk model passively heated by the central protostar cannot reproduce the observed peak brightness temperature of the 1.3 mm continuum emission (∼195 K), regardless of the assumptions about the dust opacity. Our calculation suggests that viscous accretion heating in the disk is required to reproduce the observed high brightness temperature. The observed intensity profile of the 1.3 mm dust-continuum emission along the disk minor axis is skewed toward the far side of the disk. Our modeling reveals that this asymmetric intensity distribution requires flaring of the dust along the disk vertical direction with the scale height following h / r ∼ r ^0.3 as a function of radius. These results are in sharp contrast to those of Class II disks, which show geometrically flat dust distributions and lower dust temperatures. From our modeling of the C ^18 O (2–1) emission, the outermost radius of the gas disk is estimated to be ∼80 au, which is larger than that of the dust disk (∼62 au), to reproduce the observed distribution of the C ^18 O (2–1) emission in IRS 7B-a. Our modeling unveils a hot and thick dust disk plus a larger gas disk around one of the eDisk targets, which could be applicable to other protostellar sources in contrast to more evolved sources.

Translated text

Key words

Protoplanetary Disks,Accretion Disks

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper