Galaxy Zoo Builder: Four Component Photometric decomposition of Spiral Galaxies Guided by Citizen Science

TL;DR

This paper introduces a citizen science framework within Zooniverse for multi-component photometric galaxy modeling, demonstrating its effectiveness and robustness compared to automated methods on a sample of SDSS galaxies.

Contribution

It presents a novel crowdsourcing approach for galaxy modeling that improves model accuracy and robustness over traditional automated pipelines.

Findings

Crowdsourcing can reliably produce accurate galaxy models.

The method shows good agreement or improvement over existing automated pipelines.

A catalogue of galaxy models is publicly released.

Abstract

Multi-component modelling of galaxies is a valuable tool in the effort to quantitatively understand galaxy evolution, yet the use of the technique is plagued by issues of convergence, model selection and parameter degeneracies. These issues limit its application over large samples to the simplest models, with complex models being applied only to very small samples. We attempt to resolve this dilemma of "quantity or quality" by developing a novel framework, built inside the Zooniverse citizen science platform, to enable the crowdsourcing of model creation for Sloan Digitial Sky Survey galaxies. We have applied the method, including a final algorithmic optimisation step, on a test sample of 198 galaxies, and examine the robustness of this new method. We also compare it to automated fitting pipelines, demonstrating that it is possible to consistently recover accurate models that either show good agreement with, or improve on, prior work. We conclude that citizen science is a promising technique for modelling images of complex galaxies, and release our catalogue of models.