A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos. (arXiv:2105.05859v2 [astro-ph.CO] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Hearin_A/0/1/0/all/0/1">Andrew P. Hearin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Chaves_Montero_J/0/1/0/all/0/1">Jonás Chaves-Montero</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Becker_M/0/1/0/all/0/1">Matthew R. Becker</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Alarcon_A/0/1/0/all/0/1">Alex Alarcon</a>
We present a new empirical model for the mass assembly of dark matter halos.
We approximate the growth of individual halos as a simple power-law function of
time, where the power-law index smoothly decreases as the halo transitions from
the fast-accretion regime at early times, to the slow-accretion regime at late
times. Using large samples of halo merger trees taken from high-resolution
cosmological simulations, we demonstrate that our 3-parameter model can
approximate halo growth with a typical accuracy of 0.1 dex for t > 1 Gyr for
all halos of present-day mass greater than 10^11Msun, including subhalos and
host halos in gravity-only simulations, as well as in the TNG hydrodynamical
simulation. We additionally present a new model for the assembly of halo
populations, which not only reproduces average mass growth across time, but
also faithfully captures the diversity with which halos assemble their mass.
Our python implementation is based on the autodiff library JAX, and so our
model self-consistently captures the mean and variance of halo mass accretion
rate across cosmic time. We show that the connection between halo assembly and
the large-scale density field, known as halo assembly bias, is accurately
captured by our model, and that residual errors in our approximations to halo
assembly history exhibit a negligible residual correlation with the density
field. Our publicly available source code can be used to generate Monte Carlo
realizations of cosmologically representative halo histories; our
differentiable implementation facilitates the incorporation of our model into
existing analytical halo model frameworks.
We present a new empirical model for the mass assembly of dark matter halos.
We approximate the growth of individual halos as a simple power-law function of
time, where the power-law index smoothly decreases as the halo transitions from
the fast-accretion regime at early times, to the slow-accretion regime at late
times. Using large samples of halo merger trees taken from high-resolution
cosmological simulations, we demonstrate that our 3-parameter model can
approximate halo growth with a typical accuracy of 0.1 dex for t > 1 Gyr for
all halos of present-day mass greater than 10^11Msun, including subhalos and
host halos in gravity-only simulations, as well as in the TNG hydrodynamical
simulation. We additionally present a new model for the assembly of halo
populations, which not only reproduces average mass growth across time, but
also faithfully captures the diversity with which halos assemble their mass.
Our python implementation is based on the autodiff library JAX, and so our
model self-consistently captures the mean and variance of halo mass accretion
rate across cosmic time. We show that the connection between halo assembly and
the large-scale density field, known as halo assembly bias, is accurately
captured by our model, and that residual errors in our approximations to halo
assembly history exhibit a negligible residual correlation with the density
field. Our publicly available source code can be used to generate Monte Carlo
realizations of cosmologically representative halo histories; our
differentiable implementation facilitates the incorporation of our model into
existing analytical halo model frameworks.
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