[ATM] robotic telescopes

[Peter Abrahams <telscope@europa.com>]

Computer interaction with telescopes is expanding to include machine 
learning techniques.  It is difficult to guess where this will lead, but I 
believe ATMs will play a role in some of these developments.  So, even 
though this is a little off topic, I am sending this post:

A recent journal is dedicated to the topic of robotic telescopes:
Astronomische Nachrichten 325:6-8 (2004). Special Issue: Third Potsdam 
Think Shop on Robotic Astronomy.

Definitions used in this publication:
Automatic telescopes have a 'goto' system capable of automatic acquisition 
of targets, examples include amateur Schmidt Cassegrains, which require a 
person to confirm alignment and perform other tasks.
Automated telescopes include a computer capable of executing a night's 
observing program or observations of a list of objects.  These require an 
operator to start at dusk, stop at dawn, and correct for errors or incoming 
clouds.  The instrument must find & acquire the target, confirm it is the 
correct target (in a crowded field), focus, and make the observation or 
measurement .... repeatedly.
Remote telescopes are automated telescopes operated from a distant 
location, and the systems include weather detection instruments & web cams 
to determine observing conditions.
Robotic telescopes are unmanned, they allow an operator to initiate an 
observing program, and the telescope will complete it.  Also described as 
'autonomous'.  There are levels of robotic autonomy.  Generally the 
operator is active at the beginning of a night, but less frequent 
initiation is possible.  Recovery from system errors might require human 
intervention.  Automatic scheduling involves selection of targets, based on 
optimization determined by the operator's program; and multiple observing 
programs utilizing a single telescope involve prioritizing & equitably 
distributing observing time.
Adaptive optics are automatic by nature, but system calibration is critical 
& complex; the immediate use on robotic telescopes is to shorten 
integration time on stellar sources.
Utility: Classification of objects as they are observed.  Spectroscopy; 
automated spectral analysis seems to be a big field now.  Photometry.
Locations in Antarctica are claimed to have the best seeing on earth; these 
& other inhospitable sites are opened up by robotic telescopes.

Robotic telescopes are being developed using machine learning 
technology.  (One definition of 'machine learning': Computer systems 
acquire knowledge from previous performance & results, and improve their 
performance over time.  Raw data are externally supplied; training examples 
are supplied by a previous stage of the process.  Uses pattern recognition 
software.)
These telescopes could recognize celestial transients (survey operations); 
slew to fast transients such as Gamma ray bursts; and monitor variations in 
persistent sources (recognizing changes as they happen).  'Time domain 
astronomy' is a broad classification that organizes events by their 
temporal characteristics.  Nearly all transients faster than a few minutes 
in length are terrestrial, thus target selection is a challenge.
Also: Networks of autonomous robotic telescopes; possibly using diverse 
instruments.

Are there papers about the future development of telescopes combined with 
machine learning?

=============================
Peter Abrahams   telscope@europa.com
The history of the telescope and the binocular:
     http://home.europa.com/~telscope/binotele.htm  


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