Estimating Extinction using Unsupervised Machine Learning
  Dust extinction is the most robust tracer of the gas distribution in the
interstellar medium, but measuring extinction is limited by the systematic
uncertainties involved in estimating the intrinsic colors to background stars.
In this paper we present a new technique, PNICER, that estimates intrinsic
colors and extinction for individual stars using unsupervised machine learning
algorithms. This new method aims to be free from any priors with respect to the
column density and intrinsic color distribution. It is applicable to any
combination of parameters and works in arbitrary numbers of dimensions.
Furthermore, it is not restricted to color space. Extinction towards single
sources is determined by fitting Gaussian Mixture Models along the extinction
vector to (extinction-free) control field observations. In this way it becomes
possible to describe the extinction for observed sources with probability
densities. PNICER effectively eliminates known biases found in similar methods
and outperforms them in cases of deep observational data where the number of
background galaxies is significant, or when a large number of parameters is
used to break degeneracies in the intrinsic color distributions. This new
method remains computationally competitive, making it possible to correctly
de-redden millions of sources within a matter of seconds. With the
ever-increasing number of large-scale high-sensitivity imaging surveys, PNICER
offers a fast and reliable way to efficiently calculate extinction for
arbitrary parameter combinations without prior information on source
characteristics. PNICER also offers access to the well-established NICER
technique in a simple unified interface and is capable of building extinction
maps including the NICEST correction for cloud substructure. PNICER is offered
to the community as an open-source software solution and is entirely written in
Python.
