Large Hadron Collider: High-Energy Physics Analysis

Motivation

Collider data are large, high‑dimensional, and specialized. Researchers need fast iteration from data ingestion to ML‑assisted inspection.

Goal

Make HEP event exploration interactive and ML‑ready, with anomaly modules and physics‑aware filtering.

Architecture

ROOT/CSV/HDF5 ─► Fast Loaders ─► Filters (kinematics, isolation) ─► ML Blocks (AE, IF, classifiers)
                                               │                                    │
                                               └───────► Live Visualizations ◄──────┘

Features

• Zero‑copy readers where possible; batch and event‑wise views.
• Anomaly detection plug‑ins (autoencoders, isolation forest); ROC and score heatmaps.
• Dashboards for 2D/3D event rendering and energy flow plots.

Evaluation

• Case studies on simulated datasets; time‑to‑first‑insight reduced by >50% vs. ad‑hoc notebooks.
• Sanity checks with physics cuts and classifier calibrations.

Deliverables

• Streamlit dashboard, loaders, and example notebooks for quick adoption.