How to measure galaxy star formation histories II: Nonparametric models. (arXiv:1811.03637v1 [astro-ph.GA])

How to measure galaxy star formation histories II: Nonparametric models. (arXiv:1811.03637v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Leja_J/0/1/0/all/0/1">Joel Leja</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Carnall_A/0/1/0/all/0/1">Adam C. Carnall</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Johnson_B/0/1/0/all/0/1">Benjamin D. Johnson</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Conroy_C/0/1/0/all/0/1">Charlie Conroy</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Speagle_J/0/1/0/all/0/1">Joshua S. Speagle</a>

Nonparametric star formation histories (SFHs) have long promised to be the
“gold standard” for galaxy SED modeling as they are flexible enough to describe
the full diversity of SFH shapes, whereas parametric models rule out a
significant fraction of these shapes {it a priori}. However, this flexibility
isn’t fully constrained even with high-quality observations, making it critical
to choose a well-motivated prior. Here we use the SED-fitting code Prospector
to explore the effect of different nonparametric priors by fitting SFHs to mock
UV-IR photometry generated from a diverse set of input SFHs. First, we confirm
that nonparametric SFHs recover input SFHs with less bias and return more
accurate errors than parametric SFHs. We further find that while nonparametric
SFHs robustly recover the overall shape of the input SFH, the primary
determinant of the size and shape of the posterior SFR(t) is the choice of
prior rather than the photometric noise. As a practical demonstration, we fit
the UV-IR photometry of $sim$6000 galaxies from the GAMA survey and measure
inter-prior scatters in mass (0.1 dex), SFR$_{100; mathrm{Myr}}$ (0.8 dex),
and mass-weighted ages (0.2 dex), with the bluest star-forming galaxies showing
the most sensitivity. An important distinguishing characteristic for
nonparametric models is the characteristic timescale for changes in SFR(t).
This difference controls whether galaxies are assembled in bursts or in
steady-state star formation, corresponding respectively to
(feedback-dominated/accretion-dominated) models of galaxy formation and to
(larger/smaller) confidence intervals derived from SED-fitting. High quality
spectroscopy has the potential to further distinguish between these proposed
models of SFR(t).

Nonparametric star formation histories (SFHs) have long promised to be the
“gold standard” for galaxy SED modeling as they are flexible enough to describe
the full diversity of SFH shapes, whereas parametric models rule out a
significant fraction of these shapes {it a priori}. However, this flexibility
isn’t fully constrained even with high-quality observations, making it critical
to choose a well-motivated prior. Here we use the SED-fitting code Prospector
to explore the effect of different nonparametric priors by fitting SFHs to mock
UV-IR photometry generated from a diverse set of input SFHs. First, we confirm
that nonparametric SFHs recover input SFHs with less bias and return more
accurate errors than parametric SFHs. We further find that while nonparametric
SFHs robustly recover the overall shape of the input SFH, the primary
determinant of the size and shape of the posterior SFR(t) is the choice of
prior rather than the photometric noise. As a practical demonstration, we fit
the UV-IR photometry of $sim$6000 galaxies from the GAMA survey and measure
inter-prior scatters in mass (0.1 dex), SFR$_{100; mathrm{Myr}}$ (0.8 dex),
and mass-weighted ages (0.2 dex), with the bluest star-forming galaxies showing
the most sensitivity. An important distinguishing characteristic for
nonparametric models is the characteristic timescale for changes in SFR(t).
This difference controls whether galaxies are assembled in bursts or in
steady-state star formation, corresponding respectively to
(feedback-dominated/accretion-dominated) models of galaxy formation and to
(larger/smaller) confidence intervals derived from SED-fitting. High quality
spectroscopy has the potential to further distinguish between these proposed
models of SFR(t).

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