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Introducing the BRAHMA simulation suite: Signatures of low mass black hole seeding models in cosmological simulations (2402.03626v1)

Published 6 Feb 2024 in astro-ph.GA

Abstract: The first "seeds" of supermassive black holes (BH) can range from $\sim102-106~M_{\odot}$. However, the lowest mass seeds ($\lesssim103 M_{\odot}$) are inaccessible to most cosmological simulations due to resolution limitations. We present our new BRAHMA suite of cosmological simulations that uses a novel flexible seeding approach to represent low mass seeds. Our suite consists of two types of boxes that model $\sim103~M_{\odot}$ seeds using two distinct but mutually consistent seeding prescriptions at different simulation resolutions. First, we have the highest resolution $[9~\mathrm{Mpc}]3$ (BRAHMA-9-D3) boxes that directly resolve $\sim103~M_{\odot}$ seeds and place them within halos with dense and metal poor gas. Second, we have lower-resolution and larger-volume $[18~\mathrm{Mpc}]3$ (BRAHMA-18-E4) and $\sim[36~\mathrm{Mpc}]3$ (BRAHMA-36-E5) boxes that seed their smallest resolvable $\sim104~&~105~\mathrm{M_{\odot}}$ BH descendants using new stochastic seeding prescriptions calibrated using the BRAHMA-9-D3 results. The three boxes together probe BHs between $\sim103-107 M_{\odot}$ at $z>7$ and we predict their key observables. The variation in the AGN luminosity functions is small (factors of $\sim2-3$) at the anticipated detection limits of potential future X-ray facilities ($\sim10{43} \mathrm{ergs~s{-1}}$ at $z\sim7$). Our simulations predict BHs $\sim10-100$ times heavier than expectations from local $M_*$ vs $M_{bh}$ relations, consistent with several JWST-detected AGN. For different seed models, our simulations merge BH binaries at $\sim1-15~\mathrm{kpc}$, with rates of $\sim200-2000$ per year for $\gtrsim103 M_{\odot}$ BHs, $\sim6-60$ per year for $\gtrsim104~M_{\odot}$ BHs, and up to $\sim10$ per year amongst $\gtrsim105 M_{\odot}$ BHs. These results suggest that the LISA mission has promising prospects for constraining seed models.

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