TL;DR
This paper introduces a neural network method to accurately reconstruct high-redshift quasar spectra around Lyα, enabling better constraints on the Universe's reionization history by analyzing quasar damping wings.
Contribution
The novel QSANNdRA neural network significantly improves quasar continuum reconstruction accuracy over existing PCA-based models, aiding in reionization studies.
Findings
Improved reconstruction error by 14.2% over PCA models.
Estimated neutral fractions at z=7.0851 and z=7.5413.
Results support a rapid end to reionization.
Abstract
Observations of the early Universe suggest that reionization was complete by , 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 Ly 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 Ly, 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 Ly. 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
