Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

TL;DR

This paper reviews how computational intelligence and machine learning are applied to large-scale astronomical time series data, especially in the context of upcoming surveys like LSST, highlighting challenges and future research directions.

Contribution

It provides an overview of current applications of machine learning in time-domain astronomy and discusses future challenges and research opportunities related to big astronomical data.

Findings

Machine learning is crucial for automated analysis of large astronomical datasets.

The LSST will generate unprecedented data rates requiring advanced computational methods.

Interdisciplinary collaboration is essential for handling future astronomical data challenges.

Abstract

Time-domain astronomy (TDA) is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new astronomical sky surveys. For example, the Large Synoptic Survey Telescope (LSST), which will begin operations in northern Chile in 2022, will generate a nearly 150 Petabyte imaging dataset of the southern hemisphere sky. The LSST will stream data at rates of 2 Terabytes per hour, effectively capturing an unprecedented movie of the sky. The LSST is expected not only to improve our understanding of time-varying astrophysical objects, but also to reveal a plethora of yet unknown faint and fast-varying phenomena. To cope with a change of paradigm to data-driven astronomy, the fields of astroinformatics and astrostatistics have been created recently. The new data-oriented paradigms for astronomy combine statistics, data mining, knowledge discovery, machine learning and computational intelligence, in order to provide the automated and robust methods needed for the rapid detection and classification of known astrophysical objects as well as the unsupervised characterization of novel phenomena. In this article we present an overview of machine learning and computational intelligence applications to TDA. Future big data challenges and new lines of research in TDA, focusing on the LSST, are identified and discussed from the viewpoint of computational intelligence/machine learning. Interdisciplinary collaboration will be required to cope with the challenges posed by the deluge of astronomical data coming from the LSST.