Cosmological Simulations

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SciServer hosts many public cosmological simulation datasets including Millennium, Eagle, and the recently released Indra suite of simulations.

Indra    |    Millennium

Indra

Indra is a suite of large-volume cosmological N-body simulations. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, providing excellent statistics of the large-scale features of the distribution of dark matter. The independent volumes have 1024^3 dark matter particles in a box of length 1Gpc/h. For more information about Indra datasets, see the Indra data release paper (Falck, et al 2021).

Getting Started with Indra data

The cosmological simulation datasets can be accessed by opting-in to the Cosmological Simulations science domain once you have a SciServer account.

To get started using Indra data, follow these steps:

  1. If you have not already, create an account on SciServer
  2. Log in to your SciServer Dashboard and click the Science Domains button
  3. Click on the Science Domains link
  4. Join the Cosmology Simulations science domain
  5. Return to the Dashboard Home and click on Compute to open SciServer Compute
  6. Create a new compute container, using the Cosmological Simulations image and mounting the following data volumes: Indra (FileDB), Indra Simulations, and Indra Simulations (Datascope)
  7. Open your new container and click on the indra folder

The .ipynb files in the indra folder are examples of how to use Indra data. The Indra data volume is read-only, so to make changes to the example notebooks, copy them in your persistent directory.

Indra Demo Notebooks

  • read_examples.ipynb: How to read all of the data products: snapshots of particle positions and velocities, plus pre-computed power spectra at select snapshots; Fourier modes of the coarse-gridded density field; and the halo and subhalo catalogs, including how to index the halo catalogs and retrieve IDs of particles in halos.
  • database_examples.ipynb: How to query the halo database tables, including sample queries that demonstrate how to select from one run and snapshot, one run and multiple snapshots, and one snapshot and multiple runs.
  • density_field_examples: How to compute real-space density fields from the Fourier-space density fields and from the snapshots of particle positions, as well as how to create quick slices for plots using the Shape3D functionality.
  • Shape3D_examples: How to use Shape3D objects to efficiently read subsets of particles contained in spheres, boxes, cones, and cone segments to e.g. grab all particles around (or in) a given halo or create lightcones.

Millennium Simulations

SciServer contains a mirror of the Millennium Simulations Database hosted by MPA in Germany. These databases can be accessed through the CasJobs tool that has used to disseminate catalog data from the Sloan Digital Sky Survey.

SciSerer also hosts raw simulation files containing the particles of these N-body simulations.

Getting started with Millennium Simulations Data

The Millennium Simulation datasets can be accessed by opting-in to the Cosmological Simulations science domain once you have a SciServer account.

To get started using Millennium data, follow these steps:

  1. If you have not already, create an account on SciServer
  2. Log in to your SciServer Dashboard and click the Science Domains button
  3. Click on the Science Domains link
  4. Join the Cosmology Simulations science domain
  5. Return to the Dashboard Home and click on Compute to open SciServer Compute
  6. Create a new compute container, using the Cosmological Simulations image and mounting the following data volumes: Getting Started, Virgo, Virgo (FileDB), and Indra Simulations (Datascope)
  7. Open your new container and click on the getting_started/AAS2021/CosmologicalSimulations/Virgo/ folder to see the example notebooks.

The .ipynb files in the Virgo folder are examples of how to use Millennium Simulation data. The Getting Started data volume is read-only, so to make changes to the example notebooks, copy them in your persistent directory.

Millennium Demo Notebooks

  • Demo1_MillenniumDatabase.ipynb illustrates how to execute the demo queries from the Millennium Database. It shows how to use the SciServer.CasJobs Python library to define the queries, execute them, and visualize their results in a single Jupyter notebook.
  • Demo2_HaloDensityProfiles.ipynb: Shows how to use the database to link dark matter halos to the particles they are made of. Calculates density profiles from the particles. Also, for a sample of halos stratified by mass, the notebook determines their density profile from the particles queries using our “FileDB” approach. Fit these halos to the Hernquist and NFW profiles and plot the relation between mass and scaling radius.
  • Demo3_HybridFileDB.ipynb: Shows how to retrieve particles in spheres around the most massive halos in the database without SQL by using Python code to access the files directly.

To run and save these notebooks, copy them into your Storage/persistent directory. There is one parameter, root_folder, that you should change to your username.

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