# AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning

arXiv: https://arxiv.org/abs/2109.05207 · HTML: https://mlsanity.com/paper/2109.05207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [modAL: A modular active learning framework for Python](https://mlsanity.com/paper/1805.00979/) (2018)
- [Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning](https://mlsanity.com/paper/1905.07424/) (2019)

## Built on by (most cited first, 1 of 1)

- [A review of unsupervised learning in astronomy](https://mlsanity.com/paper/2406.17316/) (2024)

## Abstract (arXiv)

AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. This technique prioritises data that offer high information gain, leading to improved performance using substantially less data. The system allows users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes. AstronomicAL enables experts to visualise domain-specific plots and key information relating both to broader context and details of a point of interest drawn from a variety of data sources, ensuring reliable labels. In addition, AstronomicAL provides functionality to explore all aspects of the training process, including custom models and query strategies. This makes the software a tool for experimenting with both domain-specific classifications and more general-purpose machine learning strategies. We illustrate using the system with an astronomical dataset due to the field's immediate need; however, AstronomicAL has been designed for datasets from any discipline. Finally, by exporting a simple configuration file, entire layouts, models, and assigned labels can be shared with the community. This allows for complete transparency and ensures that the process of reproducing results is effortless