Visualising the Dark Sky IEEE SciVis Contest 2015

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1 Visualising the Dark Sky IEEE SciVis Contest 2015 Theodoros Christoudias Christos Kallidonis Loizos Koutsantonis Christos Lemesios Lefteris Markou Constantinos Sophocleous Computational Science and Technology Research Center (CaSToRC) Cyprus Institute ABSTRACT Cosmological simulations are a cornerstone of our understanding of the Universe during its 13.7 billion year progression from small fluctuations that we see in the cosmic microwave background to today, where we are surrounded by galaxies and clusters of galaxies interconnected by a vast cosmic web. In this paper, we present our results on the 2015 IEEE Scientific Visualization Contest, which pertains to datasets derived from the Dark Sky Simulations [10]. We ingest, process and visualise cosmological data of particle clouds and halo formations in terms of positions and shed light on various properties of scientific interest including graviational potential, velocity and spin. 1 INTRODUCTION In this paper, we present our visualisation of the Dark Sky Simulations (DSS) datasets [10] as a contribution to the 2015 IEEE Scientific Visualization Contest. DSS are an ongoing series of cosmological N-body simulations, taking into consideration particles that interact only through gravitational processes, designed to provide a quantitative and accessible model of the evolution of the large-scale Universe. The contest data used in our visualisation comprise three primary types: Raw Particle Data Raw particle data describe individual particles and their properties. We use the total number of particles from the simulation in our visualisation, which is of the order of 2 million, tracked over 100 distinct timesteps. We use the particle dataset metadata from the simulation output files including the particle mass (common to all particles) as well as other physical quantities, and their associated measuring units: an identifier unique to each particle, the particle position, velocity and acceleration vectors, and the gravitational potential. Halo Catalogs We read-in and process halo catalogs databases that group sets of gravitationally-bound particles together into coherent structures along with the position, shape, and size, derived products are a number of statistics from the constituent particle distribution, such as angular momentum (spin) and relative particle concentration. Halo Merger Trees Finally, we process halo merger trees: linked individual halo catalogs, each representing a snapshot in time. christoudias@cyi.ac.cy c.kallidonis@cyi.ac.cy l.koutsantonis@cyi.ac.cy c.lemesios@cyi.ac.cy l.markou@cyi.ac.cy c.sophocleous@cyi.ac.cy IEEE Scientific Visualization Conference (SciVis) October, Chicago, Il, USA /15/$ IEEE Halo Merger Trees form a sparse graph that is used to analyse information such as the mass accretion from halos and the merger history over the whole 100 timestep temporal range. In Section 2 we outline the data ingestion and pre-processing for the raw particle (2.1) and for halo (Section 2.2) data separately. In Section 3 we describe the visualization graphics pipeline for the particle field and associated variable attributes (Section 3.1), and for the clustering of particles into halos and massive halo mergers (Section 3.2) over different simulation scale factors, corresponding to chronological progression. 2 DATA PRE-PROCESSING 2.1 Raw Particle Data The raw particle data are stored in SDF (Self-Describing File) format [8], that incorporates a human-readable ASCII header, followed by the raw binary data. Particle data snapshots (the SDF files of specific timesteps) are read using the sdfpy Python library [9]. The individual snapshot cosmological variables and simulation parameters (Hubble parameter, simulation width and scale factor) are loaded into memory and used to generate the formula for converting the particles position vectors to MegaParsecs 1 divided by the Halo parameter (Mpc/h) for consistency with the data available for halos (Section 2.2). Upon loading into memory the first snapshot, the particles to be tracked during the visualisation of the whole simulation are selected. The user is given the option to select the total number of particles available in the dataset or a subset of configurable size, with random sampling, based on available computational resources and time constraints. This allows downscaling the snapshot datasets, if fewer than the total number of particles is required for visualization. A larger number of particles provide a higher detail at the cost of greater computational requirements. For the purposes of our visualisation, the particles are not destroyed or removed but are maintained during the course of the simulation. The unique particle identifiers, which remain constant, are used to track individual particles throughout the simulation, spanning multiple timesteps and files. For scale invariance (as the simulation width changes with the time shift), a randomly selected seed particle is used as a reference point. This allows us to continuously transform into the frame of the specific timestep and focus on the relative motion of the particles. Thus, in addition to the conversion of the position vectors as discussed above, the particle positions for each snapshot are normalized to the difference between their actual position and the position of the reference particle in each snapshot. Once the particles are loaded into memory, one or more particle variable attributes can be selected for analysis. The data values for 1 A parsec (symbol: pc) is a unit of length used to measure the astronomically large distances to objects outside the Solar System. A parsec is the distance from the Sun to an astronomical object that has a parallax angle of one arcsecond. 79

2 the required variables are only loaded for the selected particles for each snapshot. The minimum and maximum values of the imported variables over the full particle set (not only the selected) and over all timesteps are determined through a search algorithm and kept in memory, in order for them to be readily available when setting the bounds for color map functions in the graphics pipeline (Section 3). Along with these extremes, also the extreme minimum and maximum values of the particles position vectors are stored and later used to set the physical outer bounds for the visualization. The end product of the data ingestion component of the processing pipeline is an in-memory array of particles for each snapshot (timestep) of the simulation. For each snapshot we store the normalized position vectors of each selected particle (converted to Mpc/h) as well as all the values of all variable attributes under consideration for each particle element. These include the gravitational potential and the velocity and acceleration vectors, along with their norms. For each variable the value extremes are also stored in a separate array, as previously described. 2.2 Halos Catalogs and Merger Trees Structures of particles are typically identified through a process known as halo finding, via local particle density estimation or via simple semi-local linking-length mechanism. Within halos (representing galaxies or clusters of galaxies), substructures are found. These structures and substructures interact, through merges, affecting the overall synthesis of the Universe. Visualizing these halo lifetime transitions can provide insight to understand observations from telescopes. To visualize the halo data, we utilize the halo listing reference table for each simulation snapshots, generated from the raw particle data files by the Rockstar halo finder software [2], and available as simulation data. The halo listing data are read in our application directly from the Web using the Web text loader feature of the PyPi ThingKing mapping software. The quantities we are interested in at the pre-processing stage are the vectors describing the halo position, mass, radius and spin parameter. As the halos are only formed by the particles in significant numbers at later times in the simulation, we incorporate the use of a user-defined cutoff for scale factor, that can specified as a parameter at runtime, after the start of the simulation to start the halo visualization. Henceforth, in the context of halos data ingestion and visualization, the term first snapshot will be used to refer to the starting snapshot we take into account beyond this cutoff. When loading the first snapshot, a user-defined number of halos are selected to be tracked, along with their descendant halos, throughout the simulation. We refer to these as the seed halos for our tracking visualization. Halos to be tracked are selected based on sorting the halos in the first or last snapshot in a descending order for the three quantities stated above: Mass, Radius or Spin. After sorting the halos, we place user-defined cuts on the variable attributes and only use a subset of the halos satisfying the conditions to allow for data exploration. Starting with the seed halos, we store chains of unique identifiers ID for descendant halos across timesteps. This allows for the tracking of halo merges: By searching for two or more halos that have the same halo descendant ID. The above steps are iterated for each timestep to build a database in memory that facilitates tracking the evolution of the seed halos and their descendants throughout the whole simulation. The rest of the details of halo ingestion are the same as those for particle ingestion. The assumption that halos in the simulation are not destroyed but just merged into larger halos allows us to use one of the selected seed halos (and then its first descendant at each timestep) as a reference point for the rest of the halos. We calculate the minimum and maximum values of the quantity being studied to bound the visualization together with the extreme position vectors. The end result of the data ingestion is an in-memory array of halo for each snapshot of the simulation after and including the first. For each snapshot we store the normalized position vectors of each halo in Mpc/h as well as all the values of the quantities under examination for each halo. These include the halos mass, radius and spin parameter. We also store the extremes stated above. 3 GRAPHICS PIPELINE 3.1 Particles The Matplotlib [4] visualisation library for Python is used for initial data exploration, and basic plotting facility. Matplotlib was selected because of the freedom and capabilities it provides in graphing complex datasets, and the interoperability with other python modules. The Matplotlib Axes3D plotting library is used for interactive 3D scatter projection of the cosmic field with individual particles mapped to a color map based on the values of the variable under examination. The live projection uses an update function which accesses onthe-fly the position vectors of the particles for each time snapshot, corresponding to different scale factors α together with the scalar value or vector magnitude for the physical quantities corresponding to each particle. The particle position extreme values and quantity value extremes were used to set the physical bounds of the simulation and the normalization color map scale respectively. To produce and export the final visualizations (static images and video animations) we couple to the Visualization Toolkit (VTK) [6] set of libraries. We opted to use VTK because it is free and open, for the flexibility and features it provides for scientific applications, the fact that bindings to interface with Python are readily available, and to allow for the subsequent use of the multi-platform data analysis and visualisation application Paraview [1, 3] to give the user full use of the provided visualization capabilities under a graphical user interface (GUI). The coupling to Python for data input and maninpulation, also allows for the interchange with other GUI scientific visualization packages such as Mayavi [7] and VISIT [11] based on user preference. For the production of the additional images, found at the end of this paper, the visualization focuses on mapping particles to colors based on user-defined attributes available in the dataset. We opted to use one million particles for clarity, so we downscaled the dataset by a factor of 2. We also produced videos to showcase the application of our visualisation pipeline to demonstrate the temporal evolution of the simulation, showing how halos are formed, using particles (10% of the total) and using the full dataset of about two million particles choreographed across the full spatial span at a single timestep corresponding to a scale factor α = 1, showing the present-day formation of particle clusters. In Figures 1, 2 and 4 we demonstrate the gravitational potential Φ, the velocity and acceleration vector magnitudes respectively. The view-point is kept unchanged for all images to allow to contrast and directly compare the resulting particle density distributions for different attributes. At scale factor α = 0.98 (Figure 2) the particles velocities for the whole dataset readily reveal the particles clustering into dark matter halos. 3.2 Halos We produce a basic underlying framework for visualizing highdimensionality datasets of halo properties. Similar to the case for particles (Section 3.1), we use the Python Export to VTK (PyEVTK) package to convert and refactor the halo data in VTK objects and then use Paraview to visualize them. Initially, each halo is considered as an individual entity, and is represented by a glyph. The color and opacity of each individual glyph is coloured based on a given attribute, e.g. mass, in each timestep and as time evolves, the radii of the spheres-halos also change based on the actual value of each halos radius at the given time step. Using this visualization 80

3 framework the researcher can interactively explore the time evolution of halo objects. As an example of potential use case of the data integration, and pre-selection capabilities and interactive visualisation potential of our graphics pipeline, in Figures 5, 6 we depict the formation of the most massive halo (at scale-factor equal to one, present day), of the simulation dataset: Halo Temporal Evolution We investigate the merger leading to the most massive structure at scale-factor one (any individual merger tree or multiple merger trees can be selected by the user based on a set of selection criteria) by illustrating the position of each halo in the dataset at multiple time steps each position colored by its scale factor. The user can gain insights regarding the temporal and spatial behaviour of structures during merges. In this particular instance we see that the major merger sequence is comprised predominantly of sub-mergers occurring at later times (closer to present time) Halo Mergers With the same filter over the halo list in place, to find the halo merger tree sequence leading to the most massive structure at present day over the whole dataset, we reproduce the merger through different eras in the Universe by plotting the velocities of the halos at their positions at each time step. The colors of the velocity vectors correspond to the mass of each structure participating in the giant merger, expressed in solar masses. A snapshot of the resulting interactive visualisation can be seen in Figure 6. Depicted is the initial spatial extend of the merger components and their displacement at different mass, over the evolution of the Universe leading to the formation of the massive structure at scale-factor equal to one. 4 IMPLEMENTATION The data were accessed remotely and pre-processed to create the visualization primitives, both for raw particle data and for halo catalogs and merger trees, on the Blue Waters supercomputer through an Educational allocation of computational resources. Our code that performs the initial pre-processing of the data is specifically tailored to this application and runs in parallel both over all timesteps and over particle or halo data when searches/filtering over parameters for extraction of sub-samples of interest is performed. The nature of the problem allows for massive parallelization to be achieved. The particle data final analysis and visualisation was performed on workstations at The Cyprus Institute using a python environment. All data were transformed to SciPy arrays, to allow for the use of readily available libraries to process and manipulate the data. The processing was done in parallel for the particles in each dataset, and for all scale factor values available. After importing the data to python they were transformed into VTK objects using the VTK software libraries, for direct interactive renders and through the use of visualisation software like Paraview as the final client. The halo data were imported using the list files for all scale factor values available in a python environment. Then, using SciPy libraries [5] we created filters to identify, specify and visualise halo mergers using the TreeId information available in the list files. In Figure 6 we present the final output for the case of the merger resulting in the most massive halo at scale factor α = 1.0 inthe DSS dataset. The particular search filter runs from the final towards smaller scale factor times udentifying and tracking the histories of all halos participating to monitor the halo evolution. The resulting final dataset containing just the halos in the merger is produced to reduce the data in memory and is automatically transformed to VTK format using the VTK libraries in python for final rendering and presentation to the user. By integrating and relying heavily on standard tools such as VTK and ParaView for the final visualization graphics pipeline, we aimed to achieve cross-platform compatibility, ease in potential extendability, and standards compliance. Deploying VTK and Paraview also allowed for the use of a server/client setup for remote viewing and manipulation of the large datasets, without necessitating the development of new software. During runtime, the visualizations reach interactive frame rates. ACKNOWLEDGEMENTS We wish to thank the National Center for Supercomputing Applications (NCSA) for the resources provided through the Blue Waters educational program High Performance Visualization for Large- Scale Scientific Data Analytics. This work was supported by the Cy-Tera Project, which is co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation. REFERENCES [1] J. Ahrens, B. Geveci, and C. Law. ParaView: An End-User Tool for Large-Data Visualization. The Visualization Handbook, page 717, [2] P. S. Behroozi, R. H. Wechsler, and H.-Y. Wu. The rockstar phasespace temporal halo finder and the velocity offsets of cluster cores. The Astrophysical Journal, 762(2):109, [3] A. Henderson et al. The ParaView Guide [4] J. D. Hunter. Matplotlib: A 2D graphics environment. Computing In Science & Engineering, 9(3):90 95, [5] E. Jones, T. Oliphant, and P. Peterson. {SciPy}: Open source scientific tools for Python [6] Kitware, Inc. The Visualization Toolkit User s Guide, January [7] P. Ramachandran and G. Varoquaux. Mayavi: 3D visualization of scientific data. Computing in Science & Engineering, 13(2):40 51, [8] J. K. Salmon and M. S. Warren. Self-Describing File (SDF) Library, June [9] S. W. Skillman, M. J. Turk, and M. S. Warren. Self-Describing File Reading in Python, [10] S. W. Skillman, M. S. Warren, M. J. Turk, R. H. Wechsler, D. E. Holz, and P. Sutter. Dark Sky Simulations: Early Data Release. arxiv preprint arxiv: , [11] V. Team et al. VISIT: Software the Delivers Parallel Interactive Visualization. Lawrence Livermore National Laboratory,

4 Figure 1: Gravitational potential Φ of particles for the first, second and final tercile of the simulation (top to bottom). Brighter areas correspond to stronger gravitational potential. 82

5 Figure 2: Velocity of particles for the first, second and final tercile of the simulation (top to bottom). Blue areas show high velocities, while yellow indicates smaller values. We note here that at early times there are high values of the velocities, while as the particles form halos the velocity drops significantly in the areas of smaller or no presence of halos. 83

6 84 Figure 3: Particle velocity vectors visualisation (at α = 0.98), depicting the clustering into halos. Colors correspond to vector velocity magnitudes in kpc/gyr.

7 Figure 4: Acceleration of particles for the first, second and final tercile of the simulation (top to bottom). Areas in red correspond to high values of the acceleration. 85

8 Figure 5: Time evolution of massive halo merger. Spatial transition of halos is tracked and visualized over time for scale factor values spanning α = 0.12 (blue) to α = 1.0 (red), with linear variation in opacity. Figure 6: Time evolution of massive halo merger. Tracks over time of halos of different masses merging into the most massive halo of the simulation at α = 1.0 (present). The color scale corresponds to halo mass (in solar masses). 86