# Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images

## TL;DR

This paper introduces a novel unsupervised clustering approach combining a convolutional autoencoder and a Self-Organising Map to efficiently analyze large radio-astronomical datasets, enabling discovery of outliers and morphological features.

## Contribution

It presents a new method that reduces training time and improves clustering of radio-astronomical images without labeled data, enhancing data exploration capabilities.

## Key findings

- Efficient clustering of radio-astronomical images using auto-encoded features.
- Effective outlier detection with SOM and K-means clustering.
- Reduced training time compared to traditional methods.

## Abstract

This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a Self-Organising Map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand for machine learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labelled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighbourhood similarity and K-means clustering of radio-astronomical features complexity. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) dataset image features which can be applied to new radio survey data.