A new method to quantify differentiate collapse models of star formation
  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.
