Abstract
AbstractThe existence of rapidly slewing robotic telescopes and fast alert distribution via the Internet is revolutionizing our capability to study the physics of fast astrophysical transients. But the salient challenge that optical time domain surveys must conquer is mining the torrent of data to recognize important transients in a scene full of normal variations. Humans simply do not have the attention span, memory, or reaction time required to recognize fast transients and rapidly respond. Autonomous robotic instrumentation with the ability to extract pertinent information from the data stream in real time will therefore be essential for recognizing transients and commanding rapid follow‐up observations while the ephemeral behavior is still present. Here we discuss how the development and integration of three technologies: (1) robotic telescope networks; (2) machine learning; and (3) advanced database technology, can enable the construction of smart robotic telescopes, which we loosely call “thinking” telescopes, capable of mining the sky in real time. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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