Q-Collider is a research platform for theory-driven collider emulation, distributed Jetson-based compute orchestration, validation scoring, and discovery-space mapping.
The system is designed to let physicists define particle theories, compile them into collider signatures, run AI-assisted emulated collider experiments, compare predictions against benchmark datasets, and visualize all candidate models inside a central Collider Discovery Map.
Q-Collider is not presented as a replacement for CERN production infrastructure. It is a scientifically structured digital twin and fast-simulation laboratory intended for model exploration, hypothesis ranking, anomaly prioritization, and sponsor-visible research workflows.
Modern particle theory work is fragmented across symbolic modeling, event generation, detector emulation, parameter scans, validation scripts, and disconnected visual tools. Q-Collider brings those layers together into one system.
A theory enters the platform as a structured model. That model is converted into particle content, decay signatures, feature vectors, map coordinates, emulation jobs, validation scores, and ranked discovery candidates.
The result is a collider command center centered on one question:
Which theory models are most promising, most testable, and most worth running next?
Researchers can define models in structured form through the UCML Theory Lab, including framework labels, particle properties, dominant channels, and signature types.
Theory models are translated into collider-facing quantities such as:
- mass peaks
- decay channels
- missing-energy flags
- predicted signal strength
- anomaly and resonance indicators
All models are placed onto the Collider Discovery Map, an interactive theory-space visualization where models are compared by embedding coordinates, discovery probability, signal strength, and validation score.
Q-Collider includes a Jetson Compute Cluster Manager for assigning emulator, validation, QUBO, and theory-scan jobs across NVIDIA Jetson nodes.
The platform includes a staged collider workflow inspired by real HEP pipelines:
- MadGraph stage
- Pythia stage
- Delphes stage
- Analysis stage
Predictions can be scored against datasets through model-level validation records, and anomaly-monitoring views help identify high-priority candidates for manual review.
Q-Collider is organized as a layered scientific workflow.
UCML Theory Definition
↓
Theory Model Registry
↓
Particles / Signatures / Corpus Links
↓
Theory Embedding Engine
↓
Collider Discovery Map
↓
Jetson Cluster Job Dispatch
↓
Collider Pipeline Execution
↓
Emulation Runs / Validation Scores / Analysis Results
↓
Discovery Candidate Ranking