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A&A Supplement series, Vol. 126, December II 1997, 547-553

Received January 6; accepted April 2, 1997

Temperature forecast and dome seeing minimization

I. A case study using a neural network model

F. Buffa and I. Porceddu

Send offprint request: I. Porceddu
Stazione Astronomica di Cagliari, Str. 54, Loc. Poggio dei Pini, I-09012 Capoterra (CA), Italy


Dome seeing may strongly deteriorate the final sharpness of a point source astronomical image, reducing its Image Quality. Both the telescope enclosure and the mirrors may contribute to the dome seeing, if air convection is induced by differences of temperature between them and external air. The prediction of the external air temperature with respect to a given time interval allows one to preset in advance the air conditioning temperature value in the telescope enclosure. With the aim to study the neural networks capabilities and limits to make short term temperature prediction, a few case studies have been carried out by using an autoregressive neural network model. The actual goal is to understand if and with which constraints a neural network model can actually be used in a NTT-like dome (i.e. telescope is in open air when observing and heat sources are highly controlled when close or inside the telescope's dome) for steering the daytime air conditioning system. We do not present any interface with an actual telescope: this paper presents a feasibility study about the forecasting methodological approach rather than its operational application to a specific telescope.

The results show that on site output prediction of a neural network are competitive with respect to a linear prediction approach.

keywords: turbulence -- methods: data analysis -- methods: statistical -- site testing

Copyright by the European Southern Observatory (ESO)