Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 472, Issue 3, Pages 3101-3114Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stx2161
Keywords
methods: data analysis; techniques: image processing; surveys; supernovae: general
Categories
Funding
- Radboud Excellence Initiative
- Fundacao para a Ciencia e a Technologia (FCT) [IF/00498/2015]
- Center for Research & Development in Mathematics and Applications (CIDMA) strategic project [UID/MAT/04106/2013]
- Enabling Green E-science for the Square Kilometer Array Research Infrastructure (ENGAGE SKA) [POCI-01-0145-FEDER-022217]
- Programa Operacional Competitividade e Internacionalizaceo (COMPETE)
- FCT, Portugal
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Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an approach based on convolutional neural networks to discriminate between true astrophysical sources and artefacts in reference-subtracted optical images. We show that relatively simple networks are already competitive with state-of-the-art systems and that their quality can further be improved via slightly deeper networks and additional pre-processing steps - eventually yielding models outperforming state-of-the-art systems. In particular, our best model correctly classifies about 97.3 per cent of all 'real' and 99.7 per cent of all 'bogus' instances on a test set containing 1942 'bogus' and 227 'real' instances in total. Furthermore, the networks considered in this work can also successfully classify these objects at hand without relying on difference images, which might pave the way for future detection pipelines not containing image subtraction steps at all.
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