Temperature forecast and dome seeing minimization - I. A case study using a neural network model

Dome seeing may strongly deteriorate the nal 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 con- vection is induced by dierences 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 car- ried out by using an autoregressive neural network model. The actual goal is to understand if and with which con- straints a neural network model can actually be used in a NTT-like dome (i.e. telescope is in open air when ob- serving 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 feasibil- ity study about the forecasting methodological approach rather than its operational application to a specic tele- scope. The results show that on site output prediction of a neural network are competitive with respect to a linear prediction approach.