Galaxy Nurseries: Crowdsourced analysis of slitless spectroscopic data

TL;DR

The Galaxy Nurseries project demonstrated that citizen scientists can effectively identify genuine emission lines in slitless spectroscopic data, enabling faster analysis and reducing the workload for professional astronomers.

Contribution

This study introduces a crowdsourcing approach for analyzing slitless spectroscopic data, showing its feasibility and effectiveness in identifying real emission lines.

Findings

414,360 classifications collected in 40 days

Broad agreement between citizen scientists and experts on emission line classification

Optimal thresholds can reduce spurious lines, speeding up professional analysis

Abstract

We present the results of Galaxy Nurseries project, which was designed to enable crowdsourced analysis of slitless spectroscopic data by volunteer citizen scientists using the Zooniverse online interface. The dataset was obtained by the WFC3 Infrared Spectroscopic Parallel (WISP) Survey collaboration and comprises NIR grism (G102 and G141) and direct imaging. Volunteers were instructed to evaluate indicated spectral features and decide whether it was a genuine emission line or more likely an artifact. Galaxy Nurseries was completed in only 40 days, gathering 414,360 classifications from 3003 volunteers for 27,333 putative emission lines. The results of Galaxy Nurseries demonstrate the feasibility of identifying genuine emission lines in slitless spectra by citizen scientists. Volunteer responses for each subject were aggregated to compute $f_{\mathrm{Real}}$, the fraction of volunteers who classified the corresponding emission line as "Real". To evaluate the accuracy of volunteer classifications, their aggregated responses were compared with independent assessments provided by members of the WISP Survey Science Team (WSST). Overall, there is a broad agreement between the WSST and volunteers' classifications, although we recognize that robust scientific analyses typically require samples with higher purity and completeness than raw volunteer classifications provide. Nonetheless, choosing optimal threshold values for $f_{\mathrm{Real}}$ allows a large fraction of spurious lines to be vetoed, substantially reducing the timescale for subsequent professional analysis of the remaining potential lines.