SNITCH: Seeking a simple, informative star formation history inference
  tool
  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 minutes) infer the parameters describing an analytic SFH model from
the emission and absorption features of a galaxy spectrum dominated by star
formation gas ionisation. SNITCH uses the Flexible Stellar Population Synthesis
models of Conroy et al. (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 customisable 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 SNITCH
is 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.
