# TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

## TL;DR

This paper introduces a large open dataset of simulated proton-proton collisions to support AI foundation models in particle physics.

## Contribution

A new open dataset of 10 million simulated collisions in a common format with labeled examples for three tasks is introduced.

## Key findings

- The dataset supports self-supervised training and evaluation of foundation models in particle physics.
- It includes realistic detector conditions and signal emulation for the sPHENIX Time Projection Chamber.
- The simulation and reconstruction chain is fully reproducible with the sPHENIX software stack.

## Abstract

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large-scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated protonproton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well-defined downstream tasks – track finding, particle identification, and noise tagging – to enable systematic evaluation of the foundation model’s adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center-of-mass energy of s=200GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high-energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.