Implement AI-Based Anomaly Detection for Astronomical Data in Stingray.jl

[youssefnabil2030]

Issue Description:
Currently, Stingray.jl lacks an automated mechanism to detect anomalies in astronomical time series data. This feature would be useful for identifying rare astrophysical events, such as unexpected X-ray bursts or irregular pulsar behavior.

Adding machine learning-based anomaly detection would enhance the software’s capabilities by automating the detection of unusual patterns in light curves.

Proposed Solution:
Integrate an Autoencoder-based anomaly detection model using Flux.jl.

Train the model on historical astronomical datasets to learn normal patterns.

Flag outliers that deviate significantly from expected behavior.

using Flux, Random

Simulated Light Curve Data (10 features)

X_train = rand(100, 10) # Training data
X_test = rand(10, 10) # Test data with potential anomalies

Define Autoencoder Model

encoder = Chain(Dense(10, 5, relu), Dense(5, 2, relu))
decoder = Chain(Dense(2, 5, relu), Dense(5, 10, relu))
autoencoder = Chain(encoder, decoder)

Loss Function (Mean Squared Error)

loss(x) = Flux.mse(autoencoder(x), x)

Training Process

opt = Flux.ADAM(0.01)
for epoch in 1:100
Flux.train!(loss, Flux.params(autoencoder), [(X_train,)], opt)
end

Test the model

reconstructed = autoencoder(X_test)
anomaly_scores = sum(abs.(X_test .- reconstructed), dims=2) # Compute anomaly scores

Flag anomalies based on threshold

threshold = quantile(anomaly_scores, 0.95)
anomalies = anomaly_scores .> threshold

println("Anomalies detected: ", anomalies)

Next Steps:
Refine the model using real astrophysical data from NASA HEASARC.

Implement visualization tools to highlight detected anomalies.

Optimize for efficiency in large datasets.

[SreeCharan1234]

Anomaly Detection in Astronomical Time Series Data

I am tackling the problem of detecting anomalies in astronomical time series data by developing a machine learning-based solution using Flux.jl in Julia. My approach is as follows:

1. Understand the Data and Simulate Light Curves

• Start with historical astronomical data and simulate light curves with multiple features.
• Split data into training and test sets, with test data containing potential anomalies.

2. Build an Autoencoder to Learn Normal Patterns

• Construct an encoder-decoder neural network using Dense layers with ReLU activation.
• The encoder compresses input features, and the decoder reconstructs them.
• The network learns typical behavior in the training dataset.

3. Train the Model

• Use mean squared error as the loss function to measure reconstruction accuracy.
• Optimize the network with ADAM optimizer over multiple epochs to ensure convergence.

4. Detect Anomalies

• Pass test data through the trained autoencoder to reconstruct it.
• Compute reconstruction errors as anomaly scores.
• Flag data points that exceed a chosen threshold (e.g., 95th percentile) as anomalies.

5. Next Steps for Refinement

• Apply the model on real astrophysical datasets from NASA HEASARC.
• Create visualizations to highlight detected anomalies.
• Optimize computation for large-scale datasets to make the method practical for real-world astronomical observations.

By following this approach, I aim to automate the identification of rare astrophysical events, such as unexpected X-ray bursts or irregular pulsar behavior, improving the capabilities of Stingray.jl for time series analysis.

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Can you assign me this task @youssefnabil2030 , or may I start working on it?