Bayesian Neural Networks for classification tasks in the Rubin big data era

TL;DR

This paper explores the use of Bayesian Neural Networks for classifying vast astronomical data streams, highlighting their accuracy and ability to quantify uncertainty, which is crucial for efficient data analysis in the Rubin Observatory era.

Contribution

It demonstrates that Bayesian Neural Networks are effective classifiers that also provide uncertainty estimates, aiding in the management of large-scale astronomical data.

Findings

BNNs achieve high classification accuracy

BNNs provide meaningful uncertainty quantification

Enhanced data analysis efficiency in large-scale surveys

Abstract

Upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will detect up to 10 million time-varying sources in the sky every night for ten years. This information will be transmitted in a continuous stream to brokers that will select the most promising events for a variety of science cases using machine learning algorithms. We study the benefits and challenges of Bayesian Neural Networks (BNNs) for this type of classification tasks. BNNs are found to be accurate classifiers which also provide additional information: they quantify the classification uncertainty which can be harnessed to analyse this upcoming data avalanche more efficiently.