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We apply a recently developed method for classifying broad absorption line quasars (BALQSOs) to the latest QSO catalogue constructed from Data Release 5 of the Sloan Digital Sky Survey. Our new hybrid classification scheme combines the power of simple metrics, supervised neural networks and visual inspection. In our view the resulting BALQSO catalogue is both more complete and more robust than all previous BALQSO catalogues, containing 3552 sources selected from a parent sample of 28,421 QSOs in the redshift range 1.7, Comment: 10 pages, 5 figures, 1 table available through the VizieR server. To appear in MNRAS

We carefully reconsider the problem of classifying broad absorption line quasars (BALQSOs) and derive a new, unbiased estimate of the intrinsic BALQSO fraction from the SDSS DR3 QSO catalogue. We first show that the distribution of objects selected by the so-called ``absorption index'' (AI) is clearly bimodal in log(AI), with only one mode corresponding to definite BALQSOs. The surprisingly high BALQSO fractions that have recently been inferred from AI-based samples are therefore likely to be overestimated. We then present two new approaches to the classification problem that are designed to be more robust than the AI, but also more complete than the traditional ``balnicity index'' (BI). Both approaches yield observed BALQSO fractions around 13.5 per cent, while a conservative third approach suggests an upper limit of 18.3 per cent. Finally, we discuss the selection biases that affect our observed BALQSO fraction. After correcting for these biases, we arrive at our final estimate of the intrinsic BALQSO fraction. This is f_BALQSO = 0.17 +/- 0.01 (stat) +/- 0.03 (sys), with an upper limit of f_BALQSO = 0.23. We conclude by pointing out that the bimodality of the log(AI) distribution may be evidence that the BAL-forming region has clearly delineated physical boundaries., 12 pages, 7 figures; accepted for publication in MNRAS; new version corrects minor typos and adds/updates some references

We have applied a Learning Vector Quantization (LVQ) algorithm to SDSS DR5 quasar spectra in order to create a large catalogue of broad absorption line quasars (BALQSOs). We first discuss the problems with BALQSO catalogues constructed using the conventional balnicity and/or absorption indices (BI and AI), and then describe the supervised LVQ network we have trained to recognise BALQSOs. The resulting BALQSO catalogue should be substantially more robust and complete than BI- or AI-based ones., Comment: 5 pages, 3 figures, to appear in the proceedings of "Classification and Discovery in Large Astronomical Surveys", Ringberg Castle, 14-17 October 2008