SpArcFiRe: SCALABLE AUTOMATED DETECTION OF SPIRAL GALAXY ARM SEGMENTS

Abstract

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".

Tracing the Spiral Structure of the Outer Milky Way with Dense Atomic Hydrogen Gas

“…On the other hand, observations of external galaxies indicate that the pitch angle can vary considerably along individual arms (Savchenko & Reshetnikov 2013;Davis & Hayes 2014;Honig & Reid 2015, and references therein). In the Milky Way, trigonometric parallax measurements of HMSFRs also found a considerable variation of pitch angles (7…”

The Most Ancient Spiral Galaxy: A 2.6-Gyr-old Disk with a Tranquil Velocity Field

“…A large fraction of local spirals can be modeled by superpositions of logarithmic spiral functions. Based on this, automatic spiral arm finding and logarithmic function fitting tools have been developed and applied in local galaxy surveys (e.g., Davis et al 2012;Davis & Hayes 2014;Shields et al 2015;Hart et al 2017). Our on-going effort of testing and adapting these tools at high-redshift will help to quantify spiral arms and to separate spiral from irregular structures objectively.…”

An automatic taxonomy of galaxy morphology using unsupervised machine learning

“…In addition, tools to automate the processing of large data sets to calculate these structural parameters such as Galapagos (Barden et al 2012) and it's multi-band variant produced by the MegaMorph project (Häußler et al 2013). Other tools use image processing techniques such as Ganalyzer and SpArcFiRe (Shamir 2011;Davis & Hayes 2014). In recent years, machine learning has once again become a prominent area of research following high-profile advances in object recognition, image classification and generative models (Goodfellow et al 2014;He et al 2016;Redmon et al 2016).…”