TOPO: Time-Ordered Provable Outputs

TL;DR

TOPO introduces a cryptographically secure framework using deterministic hashes and Merkle Trees to verify reproducibility and data integrity in astrophysical research, addressing challenges of transparency and bias.

Contribution

It presents a novel, trustless system for verifying astrophysical data analysis results, integrating blinding and reproducibility through cryptographic methods.

Findings

Successfully integrated into cosmological MCMC calculations

Provides efficient verification of datasets and outputs

Enhances reproducibility and data integrity in astrophysics

Abstract

We present TOPO (Time-Ordered Provable Outputs), a tool designed to enhance reproducibility and data integrity in astrophysical research, providing a trustless alternative to data analysis blinding. Astrophysical research frequently involves probabilistic algorithms, high computational demands, and stringent data privacy requirements, making it difficult to guarantee the integrity of results. TOPO provides a secure framework for verifying reproducible data analysis while ensuring sensitive information remains hidden. Our approach utilizes deterministic hashing to generate unique digital fingerprints of outputs, and Merkle Trees to store outputs in a time-ordered manner. This enables efficient verification of specific components or the entire dataset while making it computationally infeasible to manipulate results - thereby mitigating the risk of human interference and confirmation bias, key objectives of blinding methods. We demonstrate TOPO's utility in a cosmological context using TOPO-Cobaya, showcasing how blinding and verification can be seamlessly integrated into Markov Chain Monte Carlo calculations, rendering the process cryptographically provable. This method addresses pressing challenges in the reproducibility crisis and offers a robust, verifiable framework for astrophysical data analysis.