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SETI - Searching for Extraterestial Intelligence

A collection of notebooks that try to detect artificially inserted alien signals in a large data set. The challenge was taken from: https://www.kaggle.com/c/seti-breakthrough-listen. The dataset is also available on the kaggle website.

Table of contents

Conversavative approach

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Different approaches were tried out to reliably increase the SNR of alien signals (red boxes) in noisy images. Results can be found in /source/conservative-approach/. The most promising preprocessing step: Derivative images, was also applied on the whole dataset. But the results were not useful as input for a neural networks.

Finding clusters

If the right preprocessing steps were applied it is possible to automatically detect clusters in images with good SNR. That algorithm is implemented in /source/conservative-approach/derivatives+cluster.ipynb

Exploiting the data leak

It seems like the creators of the challenge shifted the frequency and reused part of the backgroind noise. That means if a perfect match is found, shifted and subtracted, the result would be an image without background noise. That process is shown in the notebook /source/remote-notebooks/magic-2-an-explanation.ipynb

Neural network approach

Two different neural networks were trained on the task and then fine-tuned to deliver good results. A ResNet18 and an EfficientNet. The corresponding notebooks can be found in /source/remote-notebooks/. Their architectures can be viewed in /net architectures/.

Results

ResNet delievered poor results which were not better than a no skill model.. But EfficientNet achieved good results. The initial implementation with an AUC score of 0.753 could be improved to a score of 0.800. This was achieved by using only ON measurements and applying a 1x1 Convolutional Layer as first layer.
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