论文标题
嘲笑在电源期间微弱的黑洞
Mocking Faint Black Holes during Reionization
论文作者
论文摘要
为了调查核黑洞（BHS）在电源过程中的潜在丰度和影响，我们通过对23个具有$ z = 6 $的星系属性的属性进行培训，从而产生一个神经网络，该神经网络估计其质量和增生率，在宇宙流体动力学模拟中，它们的质量为$ z = 6 $。然后，我们将模拟中的所有星系从$ z = 18 $到$ z = 5 $，而来自该网络的BHS。 As the network allows to robustly extrapolate to BH masses below those of the BH seeds, we predict a population of faint BHs with a turnover-free luminosity function, while retaining the bright (and observed) BHs, and together they predict a Universe in which intergalactic hydrogen is $15\%$ ionized at $z=6$ for a clumping factor of 5. Faint BHs may play a stronger role in H reionization without violating任何观察性约束。预计这也会对预热和 - 离子化产生影响，这与中性H中21 cm线的观察有关。我们还发现，BHS在较高的$ z $下更有效地增长，但主要遵循独立于红色速度的Galaxy-BH关系。我们提供了BHS氢电离发射率的幂律参数化。
To investigate the potential abundance and impact of nuclear black holes (BHs) during reionization, we generate a neural network that estimates their masses and accretion rates by training it on 23 properties of galaxies harbouring them at $z=6$ in the cosmological hydrodynamical simulation Massive-Black II. We then populate all galaxies in the simulation from $z=18$ to $z=5$ with BHs from this network. As the network allows to robustly extrapolate to BH masses below those of the BH seeds, we predict a population of faint BHs with a turnover-free luminosity function, while retaining the bright (and observed) BHs, and together they predict a Universe in which intergalactic hydrogen is $15\%$ ionized at $z=6$ for a clumping factor of 5. Faint BHs may play a stronger role in H reionization without violating any observational constraints. This is expected to have an impact also on pre-heating and -ionization, which is relevant to observations of the 21 cm line from neutral H. We also find that BHs grow more efficiently at higher $z$, but mainly follow a redshift-independent galaxy-BH relation. We provide a power law parametrisation of the hydrogen ionizing emissivity of BHs.
