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Abstract:

We use classifications provided by citizen scientists though Galaxy Zoo to investigate the correlation between bulge size and arm winding in spiral galaxies. Whilst the traditional spiral sequence is based on a combination of both measures, and is supposed to favour arm winding where disagreement exists, we demonstrate that, in modern usage, the spiral classifications Sa–Sd are predominantly based on bulge size, with no reference to spiral arms. Furthermore, in a volume limited sample of galaxies with both automated and visual measures of bulge prominence and spiral arm tightness, there is at best a weak correlation between the two. Galaxies with small bulges have a wide range of arm winding, while those with larger bulges favour tighter arms. This observation, interpreted as revealing a variable winding speed as a function of bulge size, may be providing evidence that the majority of spiral arms are not static density waves, but rather wind-up over time. This suggests the ‘winding problem’ could be solved by the constant reforming of spiral arms, rather than needing a static density wave. We further observe that galaxies exhibiting strong bars tend to have more loosely wound arms at a given bulge size than unbarred spirals. This observations suggests that the presence of a bar may slow the winding speed of spirals, and may also drive other processes (such as density waves) that generate spiral arms. It is remarkable that after over 170 years of observations of spiral arms in galaxies our understanding of them remains incomplete.

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.