Unsolved problems in observational astronomy. II. Focus on rapid response – mining the sky with “thinking” telescopes

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 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)

[1]  The Optical Gravitational Lensing Experiment. the Early Warning System , 1994, astro-ph/9408026.

[2]  Gerald J. Fishman,et al.  Gamma-ray bursts : 5th Huntsville Symposium, Huntsville, Alabama, 18-22 October 1999 , 2000 .

[3]  Richard G. West,et al.  The automated classification of astronomical light curves using Kohonen self-organizing maps , 2004 .

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[5]  Alexander S. Szalay,et al.  Virtual Observatories of the Future , 2001 .

[6]  Bohdan Paczynski,et al.  Small-telescope astronomy on global scales : IAU Colloquium 183, Proceedings of a Colloquium held in Kenting, Taiwan 4-8 January 2001 , 2001 .

[7]  Bohdan Paczynski.  Monitoring All Sky for Variability , 2000 .

[8]  A. Tomaney,et al.  MACHO Alert 95-30: First Real-Time Observation of Extended Source Effects in Gravitational Microlensing , 1997 .

[9]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[10]  M. Phillips,et al.  Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant , 1998, astro-ph/9805201.

[11]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[12]  K. E. McGowan,et al.  Northern Sky Variability Survey: Public Data Release , 2004, astro-ph/0401217.

[13]  R. Lupton,et al.  A Method for Optimal Image Subtraction , 1997, astro-ph/9712287.

[14]  R. Ellis,et al.  Measurements of $\Omega$ and $\Lambda$ from 42 high redshift supernovae , 1998, astro-ph/9812133.