Zooniverse

Abstract:

Deriving a simple, analytic galaxy star formation history (SFH) using observational data is a complex task without the proper tool to hand. We therefore present SNITCH, an open source code written in PYTHON, developed to quickly (2 min) infer the parameters describing an analytic SFH model from the emission and absorption features of a galaxy spectrum dominated by star formation gas ionization. SNITCH uses the Flexible Stellar Population Synthesis models of Conroy, Gunn & White (2009), the MaNGA Data Analysis Pipeline and a Markov Chain Monte Carlo method in order to infer three parameters (time of quenching, rate of quenching, and model metallicity) which best describe an exponentially declining quenching history. This code was written for use on the MaNGA spectral data cubes but is customizable by a user so that it can be used for any scenario where a galaxy spectrum has been obtained, and adapted to infer a user defined analytic SFH model for specific science cases. Herein, we outline the rigorous testing applied to SNITCH and show that it is both accurate and precise at deriving the SFH of a galaxy spectra. The tests suggest that SNITCHis sensitive to the most recent epoch of star formation but can also trace the quenching of star formation even if the true decline does not occur at an exponential rate. With the use of both an analytical SFH and only five spectral features, we advocate that this code be used as a comparative tool across a large population of spectra, either for integral field unit data cubes or across a population of galaxy spectra.

Abstract:

Abstract:

© 2019, Scuola Internazionale Superiore di Studi Avanzati. Effective classification of large datasets is a ubiquitous challenge across multiple knowledge domains. One solution gaining in popularity is to perform distributed data analysis via online citizen science platforms, such as the Zooniverse. The resulting growth in project numbers is increasing the need to improve understanding of the volunteer experience; as the sustainability of citizen science is dependent on our ability to design for engagement and usability. Here, we examine volunteer interaction with 63 projects, representing the most comprehensive collection of online citizen science project data gathered to date. Together, this analysis demonstrates how subtle project design changes can influence many facets of volunteer interaction, including when and how much volunteers interact, and, importantly, who participates. Our findings highlight the tension between designing for social good and broad community engagement, versus optimizing for scientific and analytical efficiency.