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BEACON: Burst Event Anomaly Clustering and Outlier Notification

License: GPL v2+

BEACON is a fully data-driven pipeline designed to detect unmodeled gravitational wave (GW) transients. By combining sequential autoregressive modeling (seqARIMA) and anomaly clustering, BEACON provides a low-latency and model-agnostic framework for robust burst detection.

Key Features

  • Denoising via seqARIMA modeling (seqarima)
  • Anomaly Detection using robust IQR-based statistical thresholds
  • Clustering of temporal outliers with DBSCAN
  • Statistical Evaluation via Poisson and Exponential models
  • Coincidence Analysis across multiple detectors
  • Fully compatible with streaming or batch-based analysis

Pipeline Overview 

                  ┌─────────────────────────┐
                  │        seqARIMA         │◀─── Denoising (seqarima)
                  └───────────┬─────────────┘
                              ↓
                  ┌─────────────────────────┐
                  │    Anomaly Detection    │◀─── IQR method
                  └───────────┬─────────────┘
                              ↓
                  ┌─────────────────────────┐
                  │       Clustering        │◀─── DBSCAN clustering
                  └───────────┬─────────────┘
                              ↓
                  ┌─────────────────────────┐
                  │ Significance Evaluation │◀─── Significance (λₐ, λ꜀)
                  └───────────┬─────────────┘
                              ↓
                  ┌─────────────────────────┐
                  │  Coincidence Analysis   │◀─── Across detectors
                  └─────────────────────────┘

Installation 

# In R
# If `devtools` is not installed
# install.packages(“devtools”)
devtools::install_github(
    repo = "https://github.com/OddThumb/beacon.git",
    upgrade = "never"
)

or

git clone https://github.com/OddThumb/beacon.git
cd beacon

# In R
devtools::install(".")

Usage 

# R Example
library(beacon)

# Load GW strain data
ts_H1 <- read_H5("H1.hdf5", sampling.freq = 4096)
ts_L1 <- read_H5("L1.hdf5", sampling.freq = 4096)
ts_list <- list("H1" = ts_H1, "L1" = ts_L1)

# Data batch preparation
batch_set <- batching.network(ts_list)

# Configure pipeline
cfg <- config_pipe()

# Run detection pipeline
result <- stream(batch_set = batch_set, arch_params = cfg)

# In console, 
# 1-th batch:
#   H1: λ_c=6.403, λ_N=3.333
#   L1: λ_c=6.445, λ_N=3.337
# 2-th batch:
#   H1: λ_c=6.403, λ_N=3.333
#   L1: λ_c=6.445, λ_N=3.337
# ...

⚠️ Note1: If you plan to use the PyCBC backend (e.g. functions in R/use_pycbc.R), make sure to link your environment with PyCBC installed. You can do so by specifying the path to your conda environment as shown below:

# Install reticulate to incorporate python package
install.packages("reticulate")

# Register your conda envrionment path
reticulate::use_condaenv(condaenv = "path/to/your/conda/env")

⚠️ Note2: The sophisticated vignettes for detailed usage will be delivered soon.

Python Version

A Python implementation of BEACON is now available at beacon-py.

Documentation

Example Datasets

See GWOSC compatible tools to fetch real event data.

Or data can be downloaded via list_event() and download_event().

For demo data, you can find it by:

{r} system.file("extdata", "demo_4kHz.h5", package = "beacon")

(The demo data was created by using Python’s pycbc package)

Publications

If you use BEACON in your work, please cite: Kim et al., “Autoregressive Search of Gravitational Waves: Design of low-latency search pipeline for unmodeled transients — BEACON”, submitted.

If you use only seqarima in your work, please cite: Kim et al., PRD, 2024, “Autoregressive Search of Gravitational Waves: Denoising”.