![]() ![]() Figure 1 shows a simple neural net with a fully connected three-layer “ feed-forward” architecture the data, in the form of an array of real numbers, is reprocessed as it’s transmitted from the “input layer” to a “hidden layer” and finally to the “output” layer. As one trains the neural network by feeding it many input data sets and scoring its output against the expected results, the network adjusts its parameters, thus learning how best to map the given inputs to the desired outputs. From Convolutions to CosmologyĪrtificial neural networks are, in essence, models with very many free parameters. How, then, can we accurately and efficiently compute peculiar velocities on cosmological scales? The authors of today’s paper may have found a solution in the field of machine learning: convolutional neural networks. However, any attempts to fully model the nonlinear growth of large-scale structure by hand quickly become prohibitively complex, necessitating a number of approximations and simplifications. Alternatively, we can take a theoretical approach, using perturbation theory to infer cosmic velocities from cosmic density data. To this end, a distance ladder or the Tully–Fisher and Faber–Jackson relations are viable methods, but each carry significant measurement uncertainty. ![]() To decouple peculiar motions from the Hubble flow observationally, we need a means of measuring distances that doesn’t require redshifts. One caveat, though: measuring peculiar velocities is hard. Peculiar motions are also the root cause of redshift–space distortions, and thus one requires precision measurements of peculiar velocities in order to test cosmological models using the Alcock–Paczynski effect (see here and here for deeper explanations of this technique). Peculiar velocities have been used to map the cosmic web - the vast network of filaments connecting matter on the universe’s largest scales (explored further here, here, and here) - and are linked to the dynamics of galaxy clusters and the cosmic microwave background via the kinematic Sunyaev–Zel’dovich effect. While the presence of peculiar motions spoils the simplicity of Hubble’s law, these motions can be a blessing in disguise: since diversions from the Hubble flow are caused by gravitational interactions - and therefore by the presence of matter - peculiar motions serve as excellent probes for the physics of structure in the universe. We collectively refer to these deviations from the Hubble flow as “peculiar motions” or “ peculiar velocities.” In general, a galaxy’s net motion can be attributed to a combination of the Hubble flow, the galaxy’s motion within its galaxy cluster or group, and the motion of the cluster or group itself. Unfortunately, however, this velocity–distance relation is too good to be true: due to the pesky influence of gravity, Hubble’s law is invalid in the vast majority of cases. Hubble’s law is a beautifully simple statement: a galaxy caught in the Hubble flow, moving with the expansion of the universe, should be traveling away from us at a speed proportional to its distance. Status: Published in ApJ Going with the (Hubble) Flow? Title: Cosmic Velocity Field Reconstruction Using AIįirst Author’s Institution: Sun Yat-Sen University, China We hope you enjoy this post from astrobites the original can be viewed at. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. ![]() Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. ![]()
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