Sonic Inspection of Spectra for Uncovering Hidden and Historic Quasars

Current and future spectroscopic surveys, including DESI, WEAVE, 4MOST, MOONS and PFS, will produce millions of galaxy spectra over the coming decade. Additionally, an influx of large datacubes, produced by Integral Field Units (IFUs), will each contain thousands of individual spectra. Traditional methods of visual inspection are becoming increasingly unfeasible for these large data volumes. Whilst Machine Learning (ML) tools are developed, manual data inspection is still required for tasks including, assessment of data quality, verification of fitting results, and producing the training datasets needed for ML approaches. Automated methods alone also limit discovery. They are optimised to identify known features of interest, and there is the risk of misclassification of rare or previously unknown objects. Audio inspection of spectroscopic data is an alternative method to visual inspection, with the potential to be more comfortable and efficient. For example, it may be possible to peripherally monitor sonified data, enabling other tasks to be performed simultaneously, increasing efficiency. This project will develop approaches for sonification (representing data with sound) of galaxy spectra. The initial focus will be searching for red quasars (which masquerade as galaxies) through the inspection of 1D spectra, and identifying and characterising Extended Emission Line Regions (EELRs) in spectral datacubes. The latter could help to identify 'historic' quasars which are no longer active and therefore no longer visible. The methods developed could be applied and adapted for a broad range of scientific cases extracted from the forthcoming spectroscopic surveys.