From rest-frame luminosity functions to observer-frame colourdistributions: tackling the next challenge in cosmological simulations. (arXiv:2003.11258v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Bravo_M/0/1/0/all/0/1">Matías Bravo</a> (1), <a href="http://arxiv.org/find/astro-ph/1/au:+Lagos_C/0/1/0/all/0/1">Claudia del P. Lagos</a> (1,2,3), <a href="http://arxiv.org/find/astro-ph/1/au:+Robotham_A/0/1/0/all/0/1">Aaron S. G. Robotham</a> (1,2), <a href="http://arxiv.org/find/astro-ph/1/au:+Bellstedt_S/0/1/0/all/0/1">Sabine Bellstedt</a> (1), <a href="http://arxiv.org/find/astro-ph/1/au:+Obreschkow_D/0/1/0/all/0/1">Danail Obreschkow</a> (1,2) ((1) International Centre for Radio Astronomy Research (ICRAR), (2) ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), (3) Cosmic Dawn Center (DAWN))
Galaxy spectral energy distributions (SEDs) remain among the most challenging
yet informative quantities to reproduce in simulations due to the large and
complex mixture of physical processes that shape the radiation output of a
galaxy. With the increasing number of surveys utilising broadband colours as
part of their target selection criteria, the production of realistic SEDs in
simulations is necessary for assisting in survey design and interpretation of
observations. The recent success in reproducing the observed luminosity
functions (LF) from far-UV to far-IR, using the state-of-the-art semi-analytic
model Shark and the SED generator ProSpect, represents a critical step towards
better galaxy colour predictions. We show that with Shark and ProSpect we can
closely reproduce the optical colour distributions observed in the panchromatic
GAMA survey. The treatment of feedback, star formation, central-satellite
interactions and radiation re-processing by dust are critical for this
achievement. The first three processes are responsible for the colour
bimodality, while dust attenuation defines the mean and scatter of the blue and
red populations. While a naive comparison between observation and simulations
displays the known issue of over-quenching of satellite galaxies, the
introduction of empirically-motivated observational errors and classification
from the same group finder used in GAMA greatly reduces this tension. The
introduction of random re-assignment of $sim15%$ of centrals/satellites as
satellites/centrals on the simulation classification closely resembles the
outcome of the group finder, providing a computationally less intensive method
to compare simulations with observations.
Galaxy spectral energy distributions (SEDs) remain among the most challenging
yet informative quantities to reproduce in simulations due to the large and
complex mixture of physical processes that shape the radiation output of a
galaxy. With the increasing number of surveys utilising broadband colours as
part of their target selection criteria, the production of realistic SEDs in
simulations is necessary for assisting in survey design and interpretation of
observations. The recent success in reproducing the observed luminosity
functions (LF) from far-UV to far-IR, using the state-of-the-art semi-analytic
model Shark and the SED generator ProSpect, represents a critical step towards
better galaxy colour predictions. We show that with Shark and ProSpect we can
closely reproduce the optical colour distributions observed in the panchromatic
GAMA survey. The treatment of feedback, star formation, central-satellite
interactions and radiation re-processing by dust are critical for this
achievement. The first three processes are responsible for the colour
bimodality, while dust attenuation defines the mean and scatter of the blue and
red populations. While a naive comparison between observation and simulations
displays the known issue of over-quenching of satellite galaxies, the
introduction of empirically-motivated observational errors and classification
from the same group finder used in GAMA greatly reduces this tension. The
introduction of random re-assignment of $sim15%$ of centrals/satellites as
satellites/centrals on the simulation classification closely resembles the
outcome of the group finder, providing a computationally less intensive method
to compare simulations with observations.
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