Kavli Affiliate: Nickolay Y. Gnedin
| First 5 Authors: David Robinson, Camille Avestruz, Nickolay Y. Gnedin, Vadim A. Semenov,
| Summary:
Gas cooling and heating rates are vital components of hydrodynamic
simulations. However, they are computationally expensive to evaluate exactly
with chemical networks or photoionization codes. We compare two different
approximation schemes for gas cooling and heating in an idealized simulation of
an isolated galaxy. One approximation is based on a polynomial interpolation of
a table of Cloudy calculations, as is commonly done in galaxy formation
simulations. The other approximation scheme uses machine learning for the
interpolation instead on an analytic function, with improved accuracy. We
compare the temperature-density phase diagrams of gas from each simulation run
to assess how much the two simulation runs differ. Gas in the simulation using
the machine learning approximation is systematically hotter for low-density gas
with $-3 lesssim log{(n_b/mathrm{cm}^{-3})} lesssim -1$. We find a critical
curve in the phase diagram where the two simulations have equal amounts of gas.
The phase diagrams differ most strongly at temperatures just above and below
this critical curve. We compare CII emission rates for collisions with various
particles (integrated over the gas distribution function), and find slight
differences between the two simulations. Future comparisons with simulations
including radiative transfer will be necessary to compare observable quantities
like the total CII luminosity.
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