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LStein: A new approach to visualizing sparse 2.5-dimensional data
Lukas Steinwender, Anais Möller, Christopher J. Fluke
Visualization of high-dimensional data is crucial to retrieve all the knowledge that is contained within a dataset. Effective and informative presentation of three-dimensional data…
A Machine Learning empowered search for Sub-Minute Optical Transient Events with the Deeper, Wider, Faster programme
Simon R. Goode, Sara A. Webb, Jeff Cooke +12
Optical transient surveys continue to generate increasingly large datasets, prompting the introduction of machine-learning algorithms to search for quality transient candidates eff…
Real-Time Active Learning for optimised spectroscopic follow-up: Enhancing early SN Ia classification with the Fink broker
A. Möller, E. E. O. Ishida, J. Peloton +6
Current and future surveys rely on machine learning classification to obtain large and complete samples of transients. Many of these algorithms are restricted by training samples t…
Transient Classifiers for Fink: Benchmarks for LSST
B. M. O. Fraga, C. R. Bom, A. Santos +6
The upcoming Legacy Survey of Space and Time (LSST) is expected to detect a few million transients per night, which will generate a live alert stream during the entire ten years of…
A Convolutional Neural Network Approach to Supernova Time-Series Classification
Helen Qu, Masao Sako, Anais Moller +1
One of the brightest objects in the universe, supernovae (SNe) are powerful explosions marking the end of a star's lifetime. Supernova (SN) type is defined by spectroscopic emissio…
Fink: early supernovae Ia classification using active learning
Marco Leoni, Emille E. O. Ishida, Julien Peloton +1
We describe how the Fink broker early supernova Ia classifier optimizes its ML classifications by employing an active learning (AL) strategy. We demonstrate the feasibility of impl…