Eyes on the future: optimizing science output for next generation surveys with joint crowdsourced and automated classification techniques

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

The successful use of citizen science to carry out "crowd-sourced" galaxy classifications from imaging data has shown clearly the availability of a vast resource of volunteer labor. Future surveys will also produce spectra, breaking down light into its wavelengths to reveal the physical state of the galaxies. The present, ambitious, project will extend the involvement of volunteers into these spectroscopic data, providing a richer experience for participants and addressing research in galaxy formation impossible to accomplish any other way. If the scalability problem of handling massive amounts of spectral data can also be solved this way, the impact for the community and for the scientifically literate public will be enormous. While crowdsourced galaxy classifications have proven their worth with a decade of images from the Sloan Digital Sky Survey (SDSS), several next generation surveys will be spectroscopic, which substantially increases the complexity of the data sets, while providing a much larger space for discovery. Although having spectra for tens of millions of objects will allow a wide range of science, new tools are needed to maximize the scientific return. Automatic algorithms alone will either struggle in identifying difficult features or produce highly contaminated samples to try to maximize completeness. The volume of data from forthcoming surveys renders human scrutiny impractical. This project will meet these challenges, extending to spectroscopic data the successful crowdsourcing approach used for imaging by the suite of Galaxy Zoo projects, and expanding previous NSF-supported work by this team. There will be two components: (1) Galaxy Nurseries will build an emission line catalog through crowdsourced classifications of spectroscopic data; and (2) Clump Scout will identify clumpy galaxies in the SDSS. Investigations with these catalogs will constrain galaxy formation models by (1) quantifying the fraction of galaxies with giant star-forming regions; (2) characterizing the internal variation of physical properties of such regions; and (3) comparing the gas metallicity of galaxies with and without these regions. As with the previous image work, the high-level catalogs to be produced, and the new classification algorithms to be used, are to be released publicly, and will be a valuable resource for the community. The study also continues the work to implement Galaxy Zoo in undergraduate astronomy courses, involving both graduate and undergraduate students.