A Multi-Code Analysis Toolkit for Astrophysical Simulation Data

The analysis of complex multiphysics astrophysical simulations presents a unique and rapidly growing set of challenges: reproducibility, parallelization, and vast increases in data size and complexity chief among them. In order to meet these challenges, and in order to open up new avenues for collaboration between users of multiple simulation platforms, we present yt (available at http://yt.enzotools.org/), an open source, community-developed astrophysical analysis and visualization toolkit. Analysis and visualization with yt are oriented around physically relevant quantities rather than quantities native to astrophysical simulation codes. While originally designed for handling Enzo's structure adaptive mesh refinement (AMR) data, yt has been extended to work with several different simulation methods and simulation codes including Orion, RAMSES, and FLASH. We report on its methods for reading, handling, and visualizing data, including projections, multivariate volume rendering, multi-dimensional histograms, halo finding, light cone generation and topologically-connected isocontour identification. Furthermore, we discuss the underlying algorithms yt uses for processing and visualizing data, and its mechanisms for parallelization of analysis tasks.Comment: 18 pages, 6 figures, emulateapj format. Resubmitted to Astrophysical Journal Supplement Series with revisions from referee. yt can be found at http://yt.enzotools.org

Report from the Tri-Agency Cosmological Simulation Task Force

The Tri-Agency Cosmological Simulations (TACS) Task Force was formed when Program Managers from the Department of Energy (DOE), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF) expressed an interest in receiving input into the cosmological simulations landscape related to the upcoming DOE/NSF Vera Rubin Observatory (Rubin), NASA/ESA's Euclid, and NASA's Wide Field Infrared Survey Telescope (WFIRST). The Co-Chairs of TACS, Katrin Heitmann and Alina Kiessling, invited community scientists from the USA and Europe who are each subject matter experts and are also members of one or more of the surveys to contribute. The following report represents the input from TACS that was delivered to the Agencies in December 2018.Comment: 36 pages, 3 figures. Delivered to NASA, NSF, and DOE in Dec 201

Gravitational recoil: effects on massive black hole occupation fraction over cosmic time

We assess the influence of massive black hole (MBH) ejections from galaxy centres, due to the gravitational radiation recoil, along the cosmic merger history of the MBH population. We discuss the 'danger' of the recoil for MBHs as a function of different MBH spin/orbit configurations and of the host halo cosmic bias, and on how that reflects on the 'occupation fraction' of MBHs. We assess ejection probabilities for mergers occurring in a gas-poor environment, where the MBH binary coalescence is driven by stellar dynamical processes, and the spin/orbit configuration is expected to be isotropically distributed. We contrast this case with the 'aligned' case. The latter is the most realistic situation for 'wet', gas-rich mergers, which are the expectation for high-redshift galaxies. We find that if all halos at z>5-7 host a MBH, the probability of the Milky Way (or similar size galaxy) to host a MBH today is less than 50%, unless MBHs form continuously in galaxies. The 'occupation fraction' of MBHs, intimately related to halo bias and MBH formation efficiency, plays a crucial role in increasing the retention fraction. Small halos, with shallow potential wells and low escape velocities, have a high ejection probability, but the MBH merger rate is very low along their galaxy formation merger hierarchy: MBH formation processes are likely inefficient in such shallow potential wells. Recoils can decrease the overall frequency of MBHs in small galaxies to ~60%, while they have little effect on the frequency of MBHs in large galaxies (at most a 20% effect).Comment: Accepted for publication in MNRA

Semi-Analytic Modelling of Galaxy Formation: The Local Universe

Using semi-analytic models of galaxy formation, we investigate galaxy properties such as the Tully-Fisher relation, the B and K-band luminosity functions, cold gas contents, sizes, metallicities, and colours, and compare our results with observations of local galaxies. We investigate several different recipes for star formation and supernova feedback, including choices that are similar to the treatment in Kauffmann, White & Guiderdoni (1993) and Cole et al. (1994) as well as some new recipes. We obtain good agreement with all of the key local observations mentioned above. In particular, in our best models, we simultaneously produce good agreement with both the observed B and K-band luminosity functions and the I-band Tully-Fisher relation. Improved cooling and supernova feedback modelling, inclusion of dust extinction, and an improved Press-Schechter model all contribute to this success. We present results for several variants of the CDM family of cosmologies, and find that models with values of $\Omega_0 \simeq 0.3$--0.5 give the best agreement with observations.Comment: 26 pages, LaTeX, MNRAS format, 23 inlined postscript figures. Accepted for publication in MNRAS. Revised version contains substantial changes including improved models. High resolution figures, original version, and summary of changes may be found at http://www.fiz.huji.ac.il/~rachels/papers/sp.htm

The GalMer database: Galaxy Mergers in the Virtual Observatory

We present the GalMer database, a library of galaxy merger simulations, made available to users through tools compatible with the Virtual Observatory (VO) standards adapted specially for this theoretical database. To investigate the physics of galaxy formation through hierarchical merging, it is necessary to simulate galaxy interactions varying a large number of parameters: morphological types, mass ratios, orbital configurations, etc. On one side, these simulations have to be run in a cosmological context, able to provide a large number of galaxy pairs, with boundary conditions given by the large-scale simulations, on the other side the resolution has to be high enough at galaxy scales, to provide realistic physics. The GalMer database is a library of thousands simulations of galaxy mergers at moderate spatial resolution and it is a compromise between the diversity of initial conditions and the details of underlying physics. We provide all coordinates and data of simulated particles in FITS binary tables. The main advantages of the database are VO access interfaces and value-added services which allow users to compare the results of the simulations directly to observations: stellar population modelling, dust extinction, spectra, images, visualisation using dedicated VO tools. The GalMer value-added services can be used as virtual telescope producing broadband images, 1D spectra, 3D spectral datacubes, thus making our database oriented towards the usage by observers. We present several examples of the GalMer database scientific usage obtained from the analysis of simulations and modelling their stellar population properties, including: (1) studies of the star formation efficiency in interactions; (2) creation of old counter-rotating components; (3) reshaping metallicity profiles in elliptical galaxies; (4) orbital to internal angular momentum transfer; (5) reproducing observed colour bimodality of galaxies.Comment: 15 pages, 11 figures, 10 tables accepted to A&A. Visualisation of GalMer simulations, access to snapshot files and value-added tools described in the paper are available at http://galmer.obspm.fr

A deep learning approach to halo merger tree construction

A key ingredient for semi-analytic models of galaxy formation is the mass assembly history of haloes, encoded in a tree structure. The most commonly used method to construct halo merger histories is based on the outcomes of high-resolution, computationally intensive N-body simulations. We show that machine learning (ML) techniques, in particular Generative Adversarial Networks (GANs), are a promising new tool to tackle this problem with a modest computational cost and retaining the best features of merger trees from simulations. We train our GAN model with a limited sample of merger trees from the Evolution and Assembly of GaLaxies and their Environments (EAGLE) simulation suite, constructed using two halo finders-tree builder algorithms: SUBFIND-D-TREES and ROCKSTAR-ConsistentTrees. Our GAN model successfully learns to generate well-constructed merger tree structures with high temporal resolution, and to reproduce the statistical features of the sample of merger trees used for training, when considering up to three variables in the training process. These inputs, whose representations are also learned by our GAN model, are mass of the halo progenitors and the final descendant, progenitor type (main halo or satellite), and distance of a progenitor to that in the main branch. The inclusion of the latter two inputs greatly improves the final learned representation of the halo mass growth history, especially for SUBFIND-like ML trees. When comparing equally sized samples of ML merger trees with those of the EAGLE simulation, we find better agreement for SUBFIND-like ML trees. Finally, our GAN-based framework can be utilized to construct merger histories of low-and intermediate-mass haloes, the most abundant in cosmological simulations.Fil: Robles, Sandra. Universidad Autónoma de Madrid; España. Kings College London (kcl); . University of Melbourne; AustraliaFil: Gómez, Jonathan S. Universidad Católica de Chile; Chile. Universidad Autónoma de Madrid; España. Pontificia Universidad Católica de Chile; ChileFil: Ramírez Rivera, Adín. University of Oslo; NoruegaFil: Padilla, Nelson David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Dujovne, Diego. Universidad Diego Portales; Chil