Kavli Affiliate: Mark Vogelsberger
| First 5 Authors: Matthew Gebhardt, Daniel Anglés-Alcázar, Josh Borrow, Shy Genel, Francisco Villaescusa-Navarro
| Summary:
We quantify the cosmological spread of baryons relative to their initial
neighboring dark matter distribution using thousands of state-of-the-art
simulations from the Cosmology and Astrophysics with MachinE Learning
Simulations (CAMELS) project. We show that dark matter particles spread
relative to their initial neighboring distribution owing to chaotic
gravitational dynamics on spatial scales comparable to their host dark matter
halo. In contrast, gas in hydrodynamic simulations spreads much further from
the initial neighboring dark matter owing to feedback from supernovae (SNe) and
Active Galactic Nuclei (AGN). We show that large-scale baryon spread is very
sensitive to model implementation details, with the fiducial textsc{SIMBA}
model spreading $sim$40% of baryons $>$1,Mpc away compared to $sim$10% for
the IllustrisTNG and textsc{ASTRID} models. Increasing the efficiency of
AGN-driven outflows greatly increases baryon spread while increasing the
strength of SNe-driven winds can decrease spreading due to non-linear coupling
of stellar and AGN feedback. We compare total matter power spectra between
hydrodynamic and paired $N$-body simulations and demonstrate that the baryonic
spread metric broadly captures the global impact of feedback on matter
clustering over variations of cosmological and astrophysical parameters,
initial conditions, and galaxy formation models. Using symbolic regression, we
find a function that reproduces the suppression of power by feedback as a
function of wave number ($k$) and baryonic spread up to $k sim
10,h$,Mpc$^{-1}$ while highlighting the challenge of developing models robust
to variations in galaxy formation physics implementation.
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