VSPD–HL-LHC Whitepaper

Nathan Gamal Nasser
Cloud-Native Physics Enrichment Pipeline
High-Luminosity LHC • VSPD Framework on GCP

Abstract

This whitepaper describes a cloud-native architecture for the HL-LHC Enrichment pipeline using the Vector-Based Spatial Dynamics (VSPD) framework on Google Cloud Platform. The design addresses 25 ns bunch crossing data at μ=200 pile-up conditions, applying stochastic density filtering to separate physics signals from luminosity debris, with a “Time Microscope” processing layer providing Δt temporal resolution for trajectory reconstruction.

1. Introduction

The High-Luminosity LHC (HL-LHC) will produce unprecedented data rates. A service-oriented architecture (SOA 2.0) on GCP enables scalable ingestion, enrichment, and storage. The pipeline is modeled using ColliderML-style conditions (Apache Parquet, μ=200) and visualized via an interactive dashboard.

2. Architecture Overview

2.1 Ingestion (Cloud Pub/Sub)

Cloud Pub/Sub acts as a digital shock absorber for 25 ns bunch crossing streams, decoupling producers from downstream processing and smoothing burst traffic.

2.2 Enrichment (Cloud Dataflow)

The VSPD Enrichment Layer runs on Dataflow, applying Stochastic Density Filtering to reduce luminosity debris and retain signal-like events.

2.3 Time Microscope (GKE)

Google Kubernetes Engine provides autoscaling compute with Δt temporal resolution, implementing the Time Microscope Indicator for trajectory resolution.

2.4 Storage (BigQuery)

Enriched results are stored and queried at scale via BigQuery, supporting analytics over trillions of events.

3. Key Concepts

• Cloud-Native Physics — Physics computing on elastic, pay-per-use cloud infrastructure.
• Δt Temporal Resolution — Time-domain precision for separating coincident particle interactions.
• Stochastic Density Filtering — Probabilistic separation of signal from pile-up debris.
• Time Microscope Indicator — Metric for trajectory resolution capability.
• Vector-Based Spatial Dynamics (VSPD) — Framework for spatial-temporal particle state representation.

4. Cost Efficiency

The design leverages GCP Spot VMs where appropriate to reduce computational cost while maintaining pipeline throughput and reliability.

5. Conclusion

The VSPD–HL-LHC pipeline on GCP demonstrates a viable path for cloud-native HL-LHC data enrichment, transforming luminosity debris into queryable physics signals through a modular, scalable architecture.