Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys

TL;DR

This paper discusses the development of machine learning tools for real-time detection and classification of transient astronomical events in massive data streams, aiming to enhance rapid response and scientific discovery.

Contribution

It introduces a set of machine learning methods tailored for real-time analysis of sky survey data, addressing the challenge of rapid response to transient phenomena.

Findings

Effective detection and classification of transient events in real-time.

Improved response times enable timely follow-up observations.

Framework applicable to other sensor network data streams.

Abstract

The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.