Nāhiku: Anomaly Detection in Stellar Light Curves

Nāhiku is the ʻŌlelo Hawaiʻi word for the ‘Big Dipper’ constellation.

This is an open-source package designed to simplify the detection of anomalies in stellar light curves using a principled, probabilistic approach. It provides tools for light curve modeling, prewhitening, and anomaly detection—specifically optimized for identifying “dipper” events, such as exocomets.

Features

  • Principled Modeling: Uses Gaussian Processes (GP) to model stellar variability.

  • Prewhitening: Built-in support for removing stellar pulsations using the Balmung algorithm.

  • Greedy Search: Fast iterative anomaly detection.

  • Exhaustive Search: Comprehensive search for anomalies across varying window sizes.

  • Synthetic Data: Tools to generate synthetic light curves with injected anomalies for testing and validation.

Installation

You can install nahiku directly from PyPI:

pip install nahiku

Quick Example

Here is a simple example of how to use Nāhiku to detect an injected anomaly in a synthetic light curve:

import numpy as np
from nahiku import Nahiku

# 1. Create a synthetic light curve
x = np.arange(0, 100, 0.1)
y = np.sin(x) + np.random.normal(0, 0.1, size=x.shape)

# 2. Initialize Nahiku and prewhiten the signal
nahiku = Nahiku(x, y)
nahiku.prewhiten(minimum_snr=1)

# 3. Inject an exocomet-shaped anomaly
nahiku.inject_anomaly(1, absolute_width=0.5, absolute_depth=5, shapes=["exocomet"], idxs=[350])

# 4. Search for the anomaly using Greedy Search
res = nahiku.greedy_search()
print(f"Found {res.num_detected_anomalies} anomalies.")

# 5. Visualize the results
nahiku.plot()

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Indices and tables