Self-Organizing Maps. An application to the OGLE data and the Gaia Science Alerts

TL;DR

This paper explores the use of Self-Organizing Maps (SOM) for analyzing large astronomical datasets, demonstrating its application to OGLE-III data and potential use in Gaia Science Alerts for star brightness and spectral change detection.

Contribution

It presents the application of SOM to OGLE-III data and discusses its implementation in Gaia's classification-based Science Alerts system for efficient anomaly detection.

Findings

SOM effectively clusters multi-dimensional astronomical data.

Preliminary results show SOM's potential in identifying variable stars.

The method is suitable for real-time analysis in large surveys.

Abstract

Self-Organizing Map (SOM) is a promising tool for exploring large multi-dimensional data sets. It is quick and convenient to train in an unsupervised fashion and, as an outcome, it produces natural clusters of data patterns. An example of application of SOM to the new OGLE-III data set is presented along with some preliminary results. Once tested on OGLE data, the SOM technique will also be implemented within the Gaia mission's photometry and spectrometry analysis, in particular, in so-called classification-based Science Alerts. SOM will be used as a basis of this system as the changes in brightness and spectral behaviour of a star can be easily and quickly traced on a map trained in advance with simulated and/or real data from other surveys.