Tuesdays 10:30 - 11:30 | Fridays 11:30 - 12:30
Showing votes from 2021-05-18 11:30 to 2021-05-21 12:30 | Next meeting is Tuesday Aug 26th, 10:30 am.
We show that the physical conditions which induce the Thakurta metric, recently studied by Boehm et al. in the context of time-dependent black hole masses, correspond to a single accreting black hole in the entire Universe filled with isotropic non-interacting dust. In such a case, the physics of black hole accretion is not local but tied to the properties of the entire Universe. Any density fluctuation or interaction would destroy such a picture. We do not know any realistic physical example where such conditions can be realized. In particular, this solution does not apply to black hole binaries. As cosmological black holes and their mass growth via accretion are not described by the Thakurta metric, constraints on the primordial black hole abundance from the LIGO-Virgo and the CMB measurements remain valid.
We develop a deep learning technique to infer the non-linear velocity field from the dark matter density field. The deep learning architecture we use is an "U-net" style convolutional neural network, which consists of 15 convolution layers and 2 deconvolution layers. This setup maps the 3-dimensional density field of $32^3$-voxels to the 3-dimensional velocity or momentum fields of $20^3$-voxels. Through the analysis of the dark matter simulation with a resolution of $2 {h^{-1}}{\rm Mpc}$, we find that the network can predict the the non-linearity, complexity and vorticity of the velocity and momentum fields, as well as the power spectra of their value, divergence and vorticity and its prediction accuracy reaches the range of $k\simeq1.4$ $h{\rm Mpc}^{-1}$ with a relative error ranging from 1% to $\lesssim$10%. A simple comparison shows that neural networks may have an overwhelming advantage over perturbation theory in the reconstruction of velocity or momentum fields.