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The VST Telescope Control Software logs continuously detailed information about the telescope and instrument operations. Commands, telemetries, errors, weather conditions and anything may be relevant for the instrument maintenance and the identification of problem sources is regularly saved. All information are recorded in textual form. These log files are often examined individually by the observatory personnel for specific issues and for tackling problems raised during the night. Thus, only a minimal part of the information is normally used for daily maintenance. Nevertheless, the analysis of the archived information collected over a long time span can be exploited to reveal useful trends and statistics about the telescope, which would otherwise be overlooked. Given the large size of the archive, a manual inspection and handling of the logs is cumbersome. An automated tool with an adequate user interface has been developed to scrape specific entries within the log files, process the data and display it in a comprehensible way. This pipeline has been used to scan the information collected over 5 years of telescope activity.
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We present a new method to discriminate periodic from non-periodic irregularly sampled lightcurves. We introduce a periodic kernel and maximize a similarity measure derived from information theory to estimate the periods and a discriminator factor. We tested the method on a dataset containing 100,000 synthetic periodic and non-periodic lightcurves with various periods, amplitudes and shapes generated using a multivariate generative model. We correctly identified periodic and non-periodic lightcurves with a completeness of 90% and a precision of 95%, for lightcurves with a signal-to-noise ratio (SNR) larger than 0.5. We characterize the efficiency and reliability of the model using these synthetic lightcurves and applied the method on the EROS-2 dataset. A crucial consideration is the speed at which the method can be executed. Using hierarchical search and some simplification on the parameter search we were able to analyze 32.8 million lightcurves in 18 hours on a cluster of GPGPUs. Using the sensitivity analysis on the synthetic dataset, we infer that 0.42% in the LMC and 0.61% in the SMC of the sources show periodic behavior. The training set, the catalogs and source code are all available in http://timemachine.iic.harvard.edu.
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