UC Irvine

Spiral galaxies constitute a significant fraction of galaxies observed in the local universe yet their characteristic structure is not well understood. Current methods of analysis rely on manual intervention and expertise, both of which present a significant barrier to the investigation of spiral structure at the scale of modern observational surveys. We present an automated modeling pipeline that uses the simple, one-dimensional arc analysis from SpArcFiRe to generate an initial guess for GALFIT to produce two-dimensional photometric decompositions of spiral galaxies. Our automated method is encapsulated in a Python module entitled “GalfitModule” which contains a generalized framework to handle GALFIT components and parameters. GalfitModule employs this framework to translate SpArcFiRe's output to input for GALFIT, runs GALFIT using multiple novel techniques with distributed processing, and performs an assessment of the success of our decompositions. Using this pipeline, we produce two and three component decompositions of several samples of spiral galaxies from the SDSS DR7 data release, as selected by the Galaxy Zoo team. We then assess the performance of our method, validating our results by eye, and analyze the resultant parameterization of these in bulk. Our largest sample is 28912 galaxies, of which we estimate 54.3% (about 15,700) of the models accurately map the visible structure of the original observations. We identify trends in the Sérsic indices, magnitudes, and arm-to-total flux ratios, and compare these trends to previous decomposition studies, finding general agreement in the arm-to-total flux ratio. Of the other parameters, there is evidence that our models overfit the observations, causing disagreement. Finally, we present an extension to our method which evaluates the model's pitch angle as it varies along the length of the arm.