Description
The upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will enable astronomers to discover rare and distant astrophysical transients. Host-galaxy association is crucial for selecting the most scientifically interesting transients for follow-up. While this association is currently carried out using large all-sky catalogues, LSST’s Deep Drilling Fields will probe the night sky to a greater level of faintness than these catalogues typically allow. We showcase the opportunity to use pre-existing smaller-scale, field-specific catalogues for host identification in the Deep Drilling Fields and a systematic ranking of their usefulness. These often fainter and higher-redshift catalogues allow us to probe redshift evolution and discover rarer transients from the early Universe. Utilising this data against a Dark Energy Survey (DES) sample of supernovae with pre-identified hosts in the XMM-LSS and ECDFS fields we evaluate different methods for transient-host association both for accuracy and processing speed. We also attempt to apply light data-cleaning techniques to identify and remove contaminants within our associations such as diffraction spikes and blended galaxies where the correct host would be impossible to determine with confidence. We utilise lightweight interpretable machine learning approaches, such as random forests and gradient-boosted decision trees, to generate confidence scores in our contaminant selections and associated metrics. Finally we discuss the computational expense of implementation within the LSST transient alert brokers which will require efficient fast-paced processing to handle the large stream of survey data.
