Bringing manifold learning and dimensionality reduction to SED fitters
  We show unsupervised machine learning techniques are a valuable tool for both
visualizing and computationally accelerating the estimation of galaxy physical
properties from photometric data. As a proof of concept, we use self organizing
maps (SOMs) to visualize a spectral energy distribution (SED) model library in
the observed photometry space. The resulting visual maps allow for a better
understanding of how the observed data maps to physical properties and to
better optimize the model libraries for a given set of observational data.
Next, the SOMs are used to estimate the physical parameters of 14,000 z~1
galaxies in the COSMOS field and found to be in agreement with those measured
with SED fitting. However, the SOM method is able to estimate the full
probability distribution functions for each galaxy up to about a million times
faster than direct model fitting. We conclude by discussing how this speed up
and learning how the galaxy data manifold maps to physical parameter space and
visualizing this mapping in lower dimensions helps overcome other challenges in
galaxy formation and evolution.
