Surrogate modelling the Baryonic Universe II: on forward modelling the colours of individual and populations of galaxies. Chaves-Montero, J. & Hearin, A. arXiv e-prints, 2105:arXiv:2105.05853, May, 2021. ![Surrogate modelling the Baryonic Universe II: on forward modelling the colours of individual and populations of galaxies [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Among the properties shaping the light of a galaxy, the star formation history (SFH) is one of the most challenging to model due to the variety of correlated physical processes regulating star formation. In this work, we leverage the stellar population synthesis model FSPS, together with SFHs predicted by the hydrodynamical simulation IllustrisTNG and the empirical model UNIVERSEMACHINE, to study the impact of star formation variability on galaxy colours. We start by introducing a model-independent metric to quantify the burstiness of a galaxy formation model, and we use this metric to demonstrate that UNIVERSEMACHINE predicts SFHs with more burstiness relative to IllustrisTNG. Using this metric and principal component analysis, we construct families of SFH models with adjustable variability, and we show that the precision of broad-band optical and near-infrared colours degrades as the level of unresolved short-term variability increases. We use the same technique to demonstrate that variability in metallicity and dust attenuation presents a practically negligible impact on colours relative to star formation variability. We additionally provide a model-independent fitting function capturing how the level of unresolved star formation variability translates into imprecision in predictions for galaxy colours; our fitting function can be used to determine the minimal SFH model that reproduces colours with some target precision. Finally, we show that modelling the colours of individual galaxies with percent-level precision demands resorting to complex SFH models, while producing precise colours for galaxy populations can be achieved using models with just a few degrees of freedom.
@article{chaves-montero_surrogate_2021,
title = {Surrogate modelling the {Baryonic} {Universe} {II}: on forward modelling the colours of individual and populations of galaxies},
volume = {2105},
shorttitle = {Surrogate modelling the {Baryonic} {Universe} {II}},
url = {http://adsabs.harvard.edu/abs/2021arXiv210505853C},
abstract = {Among the properties shaping the light of a galaxy, the star formation
history (SFH) is one of the most challenging to model due to the variety
of correlated physical processes regulating star formation. In this
work, we leverage the stellar population synthesis model FSPS, together
with SFHs predicted by the hydrodynamical simulation IllustrisTNG and
the empirical model UNIVERSEMACHINE, to study the impact of star
formation variability on galaxy colours. We start by introducing a
model-independent metric to quantify the burstiness of a galaxy
formation model, and we use this metric to demonstrate that
UNIVERSEMACHINE predicts SFHs with more burstiness relative to
IllustrisTNG. Using this metric and principal component analysis, we
construct families of SFH models with adjustable variability, and we
show that the precision of broad-band optical and near-infrared colours
degrades as the level of unresolved short-term variability increases. We
use the same technique to demonstrate that variability in metallicity
and dust attenuation presents a practically negligible impact on colours
relative to star formation variability. We additionally provide a
model-independent fitting function capturing how the level of unresolved
star formation variability translates into imprecision in predictions
for galaxy colours; our fitting function can be used to determine the
minimal SFH model that reproduces colours with some target precision.
Finally, we show that modelling the colours of individual galaxies with
percent-level precision demands resorting to complex SFH models, while
producing precise colours for galaxy populations can be achieved using
models with just a few degrees of freedom.},
urldate = {2021-06-04},
journal = {arXiv e-prints},
author = {Chaves-Montero, Jonas and Hearin, Andrew},
month = may,
year = {2021},
keywords = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics},
pages = {arXiv:2105.05853},
}
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In this work, we leverage the stellar population synthesis model FSPS, together with SFHs predicted by the hydrodynamical simulation IllustrisTNG and the empirical model UNIVERSEMACHINE, to study the impact of star formation variability on galaxy colours. We start by introducing a model-independent metric to quantify the burstiness of a galaxy formation model, and we use this metric to demonstrate that UNIVERSEMACHINE predicts SFHs with more burstiness relative to IllustrisTNG. Using this metric and principal component analysis, we construct families of SFH models with adjustable variability, and we show that the precision of broad-band optical and near-infrared colours degrades as the level of unresolved short-term variability increases. We use the same technique to demonstrate that variability in metallicity and dust attenuation presents a practically negligible impact on colours relative to star formation variability. We additionally provide a model-independent fitting function capturing how the level of unresolved star formation variability translates into imprecision in predictions for galaxy colours; our fitting function can be used to determine the minimal SFH model that reproduces colours with some target precision. 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In this\nwork, we leverage the stellar population synthesis model FSPS, together\nwith SFHs predicted by the hydrodynamical simulation IllustrisTNG and\nthe empirical model UNIVERSEMACHINE, to study the impact of star\nformation variability on galaxy colours. We start by introducing a\nmodel-independent metric to quantify the burstiness of a galaxy\nformation model, and we use this metric to demonstrate that\nUNIVERSEMACHINE predicts SFHs with more burstiness relative to\nIllustrisTNG. Using this metric and principal component analysis, we\nconstruct families of SFH models with adjustable variability, and we\nshow that the precision of broad-band optical and near-infrared colours\ndegrades as the level of unresolved short-term variability increases. We\nuse the same technique to demonstrate that variability in metallicity\nand dust attenuation presents a practically negligible impact on colours\nrelative to star formation variability. 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