Mining the LSST data stream for distant supernovae

The LSST (Large Synoptic Survey Telescope) will be unique in its ability to image the observable southern sky every few nights. This will generate a vast dataset of transient objects, resulting in 100,000 transient alerts every night. Among these among will be of order 50,000 Type Ia supernovae per year, which can be used as cosmological distance indicators for studying dark energy. Picking out these SNe based on their photometry is a massive data challenge that has been recognised internationally (LSSTC and LSST:UK have recently funded a series of workshops on this issue).
The PhD project will start with work on these selection algorithms, using simulations as input. The work will make use of machine learning algorithms that are currently being developed within the LSST:UK consortium (of which IH is a member). The focus will be to assess the impact of spectroscopy (i.e. redshift and transient classification information for a training sample) on the selection algorithm. Such spectroscopy is expected to be provided by the 4MOST fibre-fed multi-object spectrograph instrument, a project that Lancaster has recently joined.
The second phase of the project will be preparing for 4MOST's survey. This involves designing the software interface between LSST transient detection and placement of 4MOST's spectroscopic fibres. Sophisticated algorithms will be developed to visualise and merge priorities from the transient target list with those from 4MOST's other surveys, and to optimally configure the approximately 2000 fibres per 4MOST observation (totaling over 50 million fibre positions must be defined over 4MOST's 5-year survey).