Transient detection modelling for gravitational-wave optical transient observer (GOTO) sky survey

Given the advancement of data acquisition and telescope technology, astronomy has joined the global trend of big data and artificial intelligence in recent years. The objective of GOTO is to identify optical counterparts to gravitational wave detections. This requires obtaining many images of the sky every night, which are then systematically processes and analysed to deliver 40-million observed sources. These sources are then compared against a reference set such that new bright sources can be extracted and used to form a set of counterpart candidates. Most of the candidates will not represent real cases, with their detected changes in brightness caused by errors in data collection and/or pre-processing. To this end, the handful of real candidates has to be correctly sifted from the false-positives to allow astronomers to effectively employ follow-up observations to verify their truth. The aforementioned problem falls nicely into data classification, where multiple physical measurements of candidates are explicated as independent variables with labels given by experts as the class variable. This research is set to explore conventional techniques to analyze this specific dataset, from data preparation through to model development and evaluation. The outcome of our research not only provides a basel ine for future developments, but also pro-vides a thorough review of data characteristics. It will be also proving useful for the GOTO project in terms of shaping the approach to acquire and store data.