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Robotic Telescope Networks

Robotic Telescope Networks

In the late 1980s and mid 1990s researchers began considering the potential of a global telescope network. There are many scientific advantages to linking telescopes together in a network. Some of the advantages include resource sharing, better coverage of time-domain observations, as well as improved response times and follow-up capacity. Enabling telescopes with different instruments and capabilities to communicate reduces the cost of specialized hardware, while increasing the range of science that can be performed with that hardware. Many observation programs are greatly improved by the continuous coverage that a network can provide.

A network of telescopes is ideal for rapid follow-up of transient targets. A network with the capability to receive alerts from orbiting early-warning satellites or specialist ground based instruments like the Vera C. Rubin Observatory (previously referred to as the Large Synoptic Survey Telescope or LSST) can perform reliable, inexpensive follow-up observations with a large range of instrumentation.

Las Cumbres Observatory

LCO 2m Faulkes telescope with Milky Way background

LCO 2-meter telescope. Image credit: LCO

Las Cumbres Observatory (LCO) Global Telescope Network is making significant advancements in this field. LCO is a privately funded initiative to build a global robotic telescope network for performing science as well as education outreach activities. It is comprised of the two 2-meter Faulkes Telescopes as well as a widely distributed group of 1-meter and 0.4-meter robotic telescopes. LCO’s aim is to perform novel science that utilizes the potential of a true distributed network. Because the telescopes are operated and managed by a single administrative entity, the problems faced by many groups in relation to the exchange of telescope time do not apply to this network. Instead, the major challenge is how to coordinate and schedule the network in such a way that the complex global behavior of the system is well-optimized.

- based on work by Eric Saunders