New z>=3.6 QSOs from FIRST-SDSS DR5

Our starting sample of 8665 FIRST-DR5 pairs includes 4250 objects with spectra in DR5, 52 of these being z>=3.6 QSOs.

We found that simple supervised neural networks, trained on the sources with DR5 spectra, and using optical photometry and radio data, are very effective for identifying high-z QSOs in a sample without spectra. For the sources with DR5 spectra the technique yields a completeness (fraction of actual high-z QSOs classified as such by the neural network) of 96 per cent, and an efficiency (fraction of objects selected by the neural network as high-z QSOs that actually are high-z QSOs) of 62 per cent.