Cloud Computing with Context Cameras

TL;DR

This paper presents a method for real-time photometric calibration using a network of telescopes and wide field cameras that monitor atmospheric conditions and calibrate observations to achieve high accuracy even in variable weather.

Contribution

It introduces a system combining autonomous telescopes and context cameras for continuous, automated photometric calibration across multiple sites and conditions.

Findings

Achieves better than 5% flux calibration accuracy in cloudy conditions.

Provides real-time measurements of zero-point and transparency for calibration.

Enables automatic scheduling of photometric observations during optimal conditions.

Abstract

We summarize methods and plans to monitor and calibrate photometric observations with our autonomous, robotic network of 2m, 1m and 40cm telescopes. These are sited globally to optimize our ability to observe time-variable sources. Wide field "context" cameras are aligned with our network telescopes and cycle every 2 minutes through BVriz filters, spanning our optical range. We measure instantaneous zero-point offsets and transparency (throughput) against calibrators in the 5-12m range from the all-sky Tycho2 catalog, and periodically against primary standards. Similar measurements are made for all our science images, with typical fields of view of 0.5 degrees. These are matched against Landolt, Stetson and Sloan standards, and against calibrators in the 10-17m range from the all-sky APASS catalog. Such measurements provide pretty good instantaneous flux calibration, often to better than 5%, even in cloudy conditions. Zero-point and transparency measurements can be used to characterize, monitor and inter-compare sites and equipment. When accurate calibrations of Target against Standard fields are required, monitoring measurements can be used to select truly photometric periods when accurate calibrations can be automatically scheduled and performed.