Three new enclosures for the large RAPTOR telescopes now on site

Why a Thinking Telescope?:

The 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 do not have the ability 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. 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.

The Thinking Telescope is a concept designed to enhance analysis and observation of huge sections of the night sky and to be able to extract useful information in a timely fashion.

Three technologies must be integrated in order to create autonomous robotic telescope systems capable of finding and making more detailed follow-up observations of ephemeral source anomalies in real time.

We Haven't Come Very Far...Yet.

The knowledge extraction and discovery techniques employed in astronomy have not progressed very far from those employed by Tycho Brahe when, on 11 November 1572, he started modern time domain astronomy by discovering a bright new star that was not in his �mental catalog� of the night sky. Typically, humans still screen the reduced data even from robotic telescopes and, based on their knowledge and memory, identify candidates for follow-up observations. But modern data sets are becoming too large. Recognition of ephemeral changes of persistent sources in huge data streams and identification of fast celestial transients in the forest of non-celestial transients cannot be left to human analysts.

Information Overload

Humans simply do not have the attention span, memory, or reaction time required to monitor huge volumes of data, recognize the important variations, and promptly respond with follow-up observations. The ability of modern instrumentation to collect data at dazzling rates has pushed knowledge extraction in astronomy to a tipping point. The process of discovery must fundamentally change.