The IllustrisTNG Simulations: Public Data Release

TL;DR

The paper announces the public release of the comprehensive TNG50, TNG100, and TNG300 cosmological simulation datasets, including detailed galaxy formation data, accessible via an advanced online platform with analysis tools.

Contribution

It provides the first complete public access to the full TNG simulation datasets with new web-based analysis and visualization tools for the scientific community.

Findings

Access to 1.1 PB of simulation data with 2000 snapshots.

New web API enables on-demand visualization and analysis.

Supports local computation with browser-based interface.

Abstract

We present the full public release of all data from the TNG50, TNG100 and TNG300 simulations of the IllustrisTNG project. IllustrisTNG is a suite of large volume, cosmological, gravo-magnetohydrodynamical simulations run with the moving-mesh code Arepo. TNG includes a comprehensive model for galaxy formation physics, and each TNG simulation self-consistently solves for the coupled evolution of dark matter, cosmic gas, luminous stars, and supermassive blackholes from early time to the present day, z=0. Each of the flagship runs -- TNG50, TNG100, and TNG300 -- are accompanied by lower-resolution and dark-matter only counterparts, and we discuss scientific and numerical cautions and caveats relevant when using TNG. Full volume snapshots are available at 100 redshifts; halo and subhalo catalogs at each snapshot and merger trees are also released. The data volume now directly accessible online is ~1.1 PB, including 2,000 full volume snapshots and ~110,000 high time-resolution subbox snapshots. Data access and analysis examples are available in IDL, Python, and Matlab. We describe improvements and new functionality in the web-based API, including on-demand visualization and analysis of galaxies and halos, exploratory plotting of scaling relations and other relationships between galactic and halo properties, and a new JupyterLab interface. This provides an online, browser-based, near-native data analysis platform which supports user computation with fully local access to TNG data, alleviating the need to download large simulated datasets.