6 papers
MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning
Zafiirah Hosenie, Steven Bloemen, Paul Groot +13
Astronomers require efficient automated detection and classification pipelines when conducting large-scale surveys of the (optical) sky for variable and transient sources. Such pip…
Imbalance Learning for Variable Star Classification
Zafiirah Hosenie, Robert Lyon, Benjamin Stappers +2
The accurate automated classification of variable stars into their respective sub-types is difficult. Machine learning based solutions often fall foul of the imbalanced learning pr…
Comparing Multi-class, Binary and Hierarchical Machine Learning Classification schemes for variable stars
Zafiirah Hosenie, Robert Lyon, Benjamin Stappers +1
Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels f…
A Processing Pipeline for High Volume Pulsar Data Streams
R. J. Lyon, B. W. Stappers, L. Levin +2
Pulsar data analysis pipelines have historically been comprised of bespoke software systems, supporting the off-line analysis of data. However modern data acquisition systems are m…
Single-pulse classifier for the LOFAR Tied-Array All-sky Survey
D. Michilli, J. W. T. Hessels, R. J. Lyon +7
Searches for millisecond-duration, dispersed single pulses have become a standard tool used during radio pulsar surveys in the last decade. They have enabled the discovery of two n…
Fifty Years of Pulsar Candidate Selection: From simple filters to a new principled real-time classification approach
R. J. Lyon, B. W. Stappers, S. Cooper +2
Improving survey specifications are causing an exponential rise in pulsar candidate numbers and data volumes. We study the candidate filters used to mitigate these problems during…