Multimessenger Astronomy Correlation Platform
Advanced gravitational wave and gamma-ray burst correlation analysis using machine learning and KD-tree algorithms
Developed by Team Blueberry for the CTRL Space competition, this platform represents a breakthrough in multimessenger astronomy research, enabling real-time correlation of cosmic events to unlock the secrets of the universe.
Project Overview
Our platform addresses one of the most challenging problems in modern astrophysics: identifying correlations between different types of cosmic events in real-time. By analyzing gravitational wave detections from LIGO-Virgo and gamma-ray bursts from various space observatories, we can identify potential multimessenger events that provide unprecedented insights into cosmic phenomena.
The famous GW170817 event, which we successfully detect and correlate in our system, marked the beginning of multimessenger astronomy - providing the first confirmed observation of a neutron star merger through both gravitational waves and electromagnetic radiation.
System Architecture & Methodology
Our platform is built on a robust, modular pipeline inspired by the CTRL Space Team Blueberry system design. The architecture is divided into distinct phases, each responsible for a critical aspect of multimessenger event correlation:
- Dynamic Configuration Engine: Provides adaptive search parameters for each event type (e.g., GW, GRB, Optical), enabling physically-motivated, domain-aware candidate selection. This engine uses expert-tuned values for time and spatial windows, ensuring efficient and relevant searches.
- Standardized Data Ingestion: Fetches and transforms raw data from multiple sources (GWOSC, HEASARC, ZTF, etc.) into a unified schema. All events are standardized with fields like event_id, source, event_type, utc_time, ra_deg, dec_deg, pos_error_deg, and signal_strength, allowing seamless cross-catalog analysis.
- Adaptive Candidate Search: Implements a time-windowed, spatially-aware search for event pairs, using the configuration engine to set dynamic search radii and time windows. This dramatically reduces false positives and computational waste, focusing only on physically plausible event pairs.
- Multi-Factor Scoring Engine: Each candidate pair is scored using a transparent, physics-based approach:
- Spatio-Temporal Score: Combines spatial and temporal alignment using exponential decay functions, rewarding close matches in both space and time.
- Significance Score: Uses percentile ranking to normalize signal strengths across event types, ensuring fair comparison between, e.g., GW and GRB events.
- Contextual Score: Assesses astrophysical plausibility, such as the presence of a host galaxy at the event location.
This white-box, physics-driven methodology ensures that our results are interpretable, robust, and scientifically meaningful—avoiding the pitfalls of black-box AI. The system is designed for extensibility, allowing new event types and data sources to be integrated with minimal changes.
Key Features & Innovations
- • Adaptive, domain-aware configuration for each event type
- • Unified, standardized data ingestion from multiple catalogs
- • Efficient, physically-motivated candidate search algorithm
- • Multi-factor, transparent scoring system for robust confidence estimation
- • Extensible design for future event types and data sources
- • Comprehensive error handling and quality assurance
- • Interactive visualization and reporting for scientific analysis
Advanced Correlation Algorithm
Our correlation system employs a sophisticated MultimessengerCorrelatorthat uses KD-tree spatial indexing for efficient astronomical coordinate matching. The algorithm evaluates correlations based on three weighted criteria:
Temporal Correlation (50%)
Time difference analysis with exponential decay scoring, considering arrival time delays from cosmic events traveling at the speed of light.
Spatial Correlation (30%)
Angular separation calculation using spherical coordinates, accounting for detector position uncertainties and error circles.
Significance Score (20%)
Signal strength evaluation considering SNR for gravitational waves and flux measurements for gamma-ray bursts.
The system guarantees identification of the top correlations through adaptive parameter expansion, ensuring robust detection even for weak or distant events.
Data Sources & Coverage
Gravitational Wave Events (GWOSC)
Our GWOSC dataset includes 90 confirmed GW events compiled from the official LIGO–Virgo catalogs (GWTC-1, GWTC-2.1, GWTC-3) and spans observations from the landmark detection GW150914 (2015) through recent events. The events include both binary black hole mergers and neutron star mergers.
- • Data sourced from LIGO–Virgo Open Science Center (GWOSC) catalogs
- • Covers events from 2015 to 2023
- • Binary black hole and neutron star mergers
- • Position data provided where available, though most events lack precise sky localization
- • Signal-to-noise ratios (SNR) range from 4.5 to 33
Gamma-Ray Burst Events (HEASARC)
Our HEASARC dataset compiles ~11,000 GRB events from the SwiftGRB and GRBCAT archives. It spans detections from the 1960s through the modern Swift era, covering both historical bursts and recent well-localized events.
- • Data sourced from HEASARC SwiftGRB and GRBCAT catalogs
- • Covers events from 1967 to recent years
- • Positions available for ~6,800 bursts, with RA/Dec provided where measured
- • Positional uncertainties (pos_error_deg) assigned with realistic values:
- Swift BAT: ~0.02–0.07° (arcmin-scale)
- Historical GRBs: ~0.5–2° (large error boxes)
- Transitional (2000s): ~0.1–0.5°
• Signal strength normalized (0–1), derived from peak flux/fluence when available.
Transient Name Server (TNS) Events
Our TNS dataset contains hundreds of confirmed transient and supernova events, obtained from the official IAU Transient Name Server and standardized from parquet to CSV for analysis. These events include a wide variety of astronomical transients such as Astronomical Transients (AT), Type II Supernovae (SN II), and Type Ic Supernovae (SN Ic), with discovery dates spanning multiple years.
- • Official IAU Transient Name Server (TNS) catalog
- • Event classes include AT, SN II, SN Ic, and others
- • Sky positions (RA, Dec) provided with arcsecond-level precision
- • Positional uncertainties typically ~5.6×10⁻⁵° (≃0.2 arcsec) for most entries
- • Reported magnitudes / signal strengths range from ~18 to ~27
VizieR Catalog Events (Vizier/Other Archives)
Our VizieR dataset compiles astrophysical transient events from multiple archival catalogs, standardized into a clean format for analysis and cross-correlation.
- • Data sourced from VizieR online service and associated catalogs
- • Includes positions (RA/Dec) with calibrated uncertainties
- • Event coverage spans multiple decades of astronomical surveys
- • Parameters include fluxes, magnitudes, and derived uncertainties
- • Cleaned and harmonized into a structured CSV format for research use
This dataset provides a unified, machine-readable collection of transient event parameters, enabling integration with GW and GRB datasets for multi-messenger astrophysics studies.
Platform Features
🕒 Real-time Analysis
Sub-second correlation analysis with adaptive time windows from hours to months
🗂️ Interactive Timeline
Circular timeline interface for exploring events across multiple years (2015-2025)
📊 Confidence Scoring
Multi-factor confidence scores with detailed breakdown of correlation metrics
🎯 Spatial Accuracy
Precise angular separation calculations with error circle validation
🔒 Admin Dashboard
Secure administrative interface for data management and system monitoring
📡 REST API
Comprehensive API endpoints for correlation analysis and data retrieval
Scientific Impact & Future Development
This platform represents a significant advancement in multimessenger astronomy research, providing tools that can accelerate the discovery of cosmic phenomena. Our correlation algorithms have successfully identified the historic GW170817-GRB170817A correlation, demonstrating the system's capability to detect genuine multimessenger events.
🚀 Future Enhancements
- • Integration with next-generation gravitational wave detectors (KAGRA, Einstein Telescope)
- • Machine learning models for automated event classification and significance assessment
- • Real-time alert system for immediate follow-up observations
- • Extended coverage to include neutrino detections and optical transients
- • Collaborative features for international research team coordination
About Team Blueberry
Members: Shivaprasad Gowda, Rudra Pratap Singh, Yash Verdhan, Vedansh Madan, Nakul Bhadade
Team Blueberry developed this platform as part of the CTRL Space competition, demonstrating our commitment to advancing space technology and astronomical research. Our multidisciplinary team combines expertise in astrophysics, software engineering, and data science to create innovative solutions for the space research community.
"Pushing the boundaries of multimessenger astronomy through innovative technology and collaborative research"
— Team Blueberry