The Arrowhead Mini-Supercluster of Galaxies

Superclusters of galaxies can be defined kinematically from local evaluations of the velocity shear tensor. The location where the smallest eigenvalue of the shear is positive and maximal defines the center of a basin of attraction. Velocity and density fields are reconstructed with Wiener Filter techniques. Local velocities due to the density field in a restricted region can be separated from external tidal flows, permitting the identification of boundaries separating inward flows toward a basin of attraction and outward flows. This methodology was used to define the Laniakea Supercluster that includes the Milky Way. Large adjacent structures include Perseus-Pisces, Coma, Hercules, and Shapley but current kinematic data are insufficient to capture their full domains. However there is a small region trapped between Laniakea, Perseus-Pisces, and Coma that is close enough to be reliably characterized and that satisfies the kinematic definition of a supercluster. Because of its shape, it is given the name the Arrowhead Supercluster. This entity does not contain any major clusters. A characteristic dimension is ~25 Mpc and the contained mass is only ~10^15 Msun.Comment: Accepted for publication in The Astrophysical Journal. Video can be viewed at http://irfu.cea.fr/arrowhea

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral

Reconstructing Probability Distributions with Gaussian Processes

Modern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete. Here we present a method for reconstructing the log-probability distributions of completed experiments from an existing MCMC chain (or any set of posterior samples). The reconstruction is performed using Gaussian process regression for interpolating the log-probability. This allows for easy resampling, importance sampling, marginalization, testing different samplers, investigating chain convergence, and other operations. As an example use-case, we reconstruct the posterior distribution of the most recent Planck 2018 analysis. We then resample the posterior, and generate a new MCMC chain with forty times as many points in only thirty minutes. Our likelihood reconstruction tool can be found online at https://github.com/tmcclintock/AReconstructionTool.Comment: 7 pages, 4 figures, repository at https://github.com/tmcclintock/AReconstructionToo

Free Form Lensing Implications for the Collision of Dark Matter and Gas in the Frontier Fields Cluster MACSJ0416.1-2403

We present a free form mass reconstruction of the massive lensing cluster MACSJ0416.1-2403 using the latest Hubble Frontier Fields data. Our model independent method finds that the extended lensing pattern is generated by two elongated, closely projected clusters of similar mass. Our lens model identifies new lensed images with which we improve the accuracy of the dark matter distribution. We find that the bimodal mass distribution is nearly coincident with the bimodal X-ray emission, but with the two dark matter peaks lying closer together than the centroids of the X-ray emisison. We show this can be achieved if the collision has occurred close to the plane and such that the cores are deflected around each other. The projected mass profiles of both clusters are well constrained because of the many interior lensed images, leading to surprisingly flat mass profiles of both components in the region 15-100 kpc. We discuss the extent to which this may be generated by tidal forces in our dynamical model which are large during an encounter of this type as the cores "graze" each other. The relative velocity between the two cores is estimated to be about 1200 km/s and mostly along the line of sight so that our model is consistent with the relative redshift difference between the two cD galaxies (dz = 0.04).Comment: 22 pages, 18 figures, 2 table