Scalable Automated Detection of Spiral Galaxy Arm Segments
  Given an approximately centered image of a spiral galaxy, we describe an
entirely automated method that finds, centers, and sizes the galaxy and then
automatically extracts structural information about the spiral arms. For each
arm segment found, we list the pixels in that segment and perform a
least-squares fit of a logarithmic spiral arc to the pixels in the segment. The
algorithm takes about 1 minute per galaxy, and can easily be scaled using
parallelism. We have run it on all ~644,000 Sloan objects classified as
"galaxy" and large enough to observe some structure. Our algorithm is stable in
the sense that the statistics across a large sample of galaxies vary smoothly
based on algorithmic parameters, although results for individual galaxies can
sometimes vary in a non-smooth but easily understood manner. We find a very
good correlation between our quantitative description of spiral structure and
the qualitative description provided by humans via Galaxy Zoo. In addition, we
find that pitch angle often varies significantly segment-to-segment in a single
spiral galaxy, making it difficult to define "the" pitch angle for a single
galaxy. Finally, we point out how complex arm structure (even of long arms) can
lead to ambiguity in defining what an "arm" is, leading us to prefer the term
"arm segments".
