Observations of the early Universe suggest that reionization was complete by z ∼ 6, however, the exact history of this process is still unknown. One method for measuring the evolution of the neutral fraction throughout this epoch is via observing the Lyα damping wings of high-redshift quasars. In order to constrain the neutral fraction from quasar observations, one needs an accurate model of the quasar spectrum around Lyα, after the spectrum has been processed by its host galaxy but before it is altered by absorption and damping in the intervening IGM. In this paper, we present a novel machine learning approach, using artificial neural networks, to reconstruct quasar continua around Lyα. Our QSANNDRA algorithm improves the error in this reconstruction compared to the state-of-the-art PCA-based model in the literature by 14.2% on average, and provides an improvement of 6.1% on average when compared to an extension thereof. In comparison with the extended PCA model, QSANNDRA further achieves an improvement of 22.1% and 16.8% when evaluated on low-redshift quasars most similar to the two high-redshift quasars under consideration, ULAS J1120+0641 at z = 7.0851 and ULAS J1342+0928 at z = 7.5413, respectively. Using our more accurate reconstructions of these two z > 7 quasars, we estimate the neutral fraction of the IGM using a homogeneous reionization model and find x¯H1=0.25+0.05−0.05 at z = 7.0851 and x¯H1=0.60+0.11−0.11 at z = 7.5413. Our results are consistent with the literature and favour a rapid end to reionization
