A new method to quantify differentiate collapse models of star formation

TL;DR

This paper introduces $COREGA$, a new tool for analyzing dust continuum data to characterize dense star-forming cores and distinguish between different collapse models with high-resolution observations.

Contribution

The paper presents $COREGA$, a novel method that accurately models dense cores and differentiates collapse models using multi-wavelength dust emission data.

Findings

$COREGA$ reliably estimates core properties from simulated data.

Results are stable against observational noise within certain levels.

High-resolution ALMA data can distinguish collapse models.

Abstract

Continuum emissions from dust grains are used as a general probe to constrain the initial physical conditions of molecular dense cores where new stars may born. To get as much information as possible from dust emissions, we have developed a tool, named as $COREGA$, which is capable of identifying positions of dense cores, optimizing a three-dimensional model for the dense cores with well characterized uncertainties. $COREGA$ can also estimate the physical properties of dense cores, such as density, temperature, and dust emissivity, through analyzing multi-wavelength dust continuum data sets. In the numerical tests on $COREGA$, the results of fitting simulated data are consistent with initial built-in parameters. We also demonstrate $COREGA$ by adding random gaussian noises with Monte Carlo methods and show that the results are stable against varying observational noise intensities within certain levels. A beam size $<$ 3 arcsec and rms $<$ 0.2mJy/pixel (1 pixel = 0.1") is needed for ALMA to distinguish different collapse models, such as power law and Bonner-Ebert sphere, during continuum observations of massive dense cores in Orion molecular cloud. Based on its advanced algorithm, $COREGA$ is capable of giving a quick and deep analysis on dust cores.