Digging Deeper: Algorithms for Computationally-Limited Searches in Astronomy

Abstract

Astronomy, like most other fields, is being deluged by exponentially growing streams of ever more complex data. While these massive data streams bring a great discovery potential, their full scientific exploitation poses many challenges, due to both data volumes and data complexity. Moreover, the need to discover and characterize interesting, faint signals in such data streams quickly and robustly, in order to deploy costly follow-up resources that are often necessary for the full scientific returns, makes the challenges even sharper. Examples in astronomy include transient events and variable sources found in digital synoptic sky surveys, gravitational wave signals, faint radio transients, pulsars, and other types of variable sources in the next generation of panoramic radio surveys, etc. Similar situations arise in the context of space science and planetary exploration, environmental monitoring, security, etc. In most cases, rapid discovery and characterization of interesting signals is highly computationally limited. The goal of this study was to define a number of interesting, often mission-critical challenges of this nature in the broader context of time-domain astronomy, but with an eye on their applicability elsewhere.