# A new paradigm for reproducing and analyzing N-body simulations of planetary systems

## TL;DR

This paper introduces open-source tools that enable exact reproducibility and flexible analysis of N-body simulations of planetary systems, significantly improving reproducibility and data accessibility.

## Contribution

The authors present a novel framework that ensures bit-by-bit reproducibility of N-body experiments and allows dynamic data extraction during simulations.

## Key findings

- Achieved exact reproducibility of a 10-billion-year Solar System simulation
- Enabled real-time data access and analysis during simulations
- Reduced analysis time from weeks to seconds

## Abstract

The reproducibility of experiments is one of the main principles of the scientific method. However, numerical N-body experiments, especially those of planetary systems, are currently not reproducible. In the most optimistic scenario, they can only be replicated in an approximate or statistical sense. Even if authors share their full source code and initial conditions, differences in compilers, libraries, operating systems or hardware often lead to qualitatively different results. We provide a new set of easy-to-use, open-source tools that address the above issues, allowing for exact (bit-by-bit) reproducibility of N-body experiments. In addition to generating completely reproducible integrations, we show that our framework also offers novel and innovative ways to analyze these simulations. As an example, we present a high-accuracy integration of the Solar System spanning 10Gyrs, requiring several weeks to run on a modern CPU. In our framework we can not only easily access simulation data at predefined intervals for which we save snapshots, but at any time during the integration. We achieve this by integrating an on-demand reconstructed simulation forward in time from the nearest snapshot. This allows us to extract arbitrary quantities at any point in the saved simulation exactly (bit-by-bit), and within seconds rather than weeks. We believe that the tools we present in this paper offer a new paradigm for how N-body simulations are run, analyzed, and shared across the community.