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Title: Reionization history constraints from neural network based predictions of high-redshift quasar continua

Abstract

ABSTRACT 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 intergalactic medium (IGM). In this paper, we present a novel machine learning approach, using artificial neural networks, to reconstruct quasar continua around Lyα. Our Quasar Spectra from Artificial Neural Network based predictive Regression Algorithm(QSANNdRA) improves the error in this reconstruction compared to the state-of-the-art principal component analysis (PCA) based model in the literature by 14.2 per cent on average, and provides an improvement of 6.1 per cent on average when compared to an extension thereof. In comparison with the extended PCA model, QSANNdRA further achieves an improvement of 22.1 per cent and 16.8 per cent 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 $$\bar{x}_\mathrm{H\, \small{I}} = 0.25^{+0.05}_{-0.05}$$ at z = 7.0851 and $$\bar{x}_\mathrm{H\, \small{I}} = 0.60^{+0.11}_{-0.11}$$ at z = 7.5413. Our results are consistent with the literature and favour a rapid end to reionization.

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [4];  [2];  [2]
  1. New College, University of Oxford, Holywell Street, Oxford OX1 3BN, UK
  2. Sub-department of Astrophysics, University of Oxford, Keble Road, Oxford OX1 3RH, UK
  3. Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK
  4. Lawrence Berkeley National Laboratory, Berkeley, CA 94720-8139, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1605034
Resource Type:
Published Article
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 493 Journal Issue: 3; Journal ID: ISSN 0035-8711
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Ďurovčíková, Dominika, Katz, Harley, Bosman, Sarah E. I., Davies, Frederick B., Devriendt, Julien, and Slyz, Adrianne. Reionization history constraints from neural network based predictions of high-redshift quasar continua. United Kingdom: N. p., 2020. Web. doi:10.1093/mnras/staa505.
Ďurovčíková, Dominika, Katz, Harley, Bosman, Sarah E. I., Davies, Frederick B., Devriendt, Julien, & Slyz, Adrianne. Reionization history constraints from neural network based predictions of high-redshift quasar continua. United Kingdom. doi:10.1093/mnras/staa505.
Ďurovčíková, Dominika, Katz, Harley, Bosman, Sarah E. I., Davies, Frederick B., Devriendt, Julien, and Slyz, Adrianne. Thu . "Reionization history constraints from neural network based predictions of high-redshift quasar continua". United Kingdom. doi:10.1093/mnras/staa505.
@article{osti_1605034,
title = {Reionization history constraints from neural network based predictions of high-redshift quasar continua},
author = {Ďurovčíková, Dominika and Katz, Harley and Bosman, Sarah E. I. and Davies, Frederick B. and Devriendt, Julien and Slyz, Adrianne},
abstractNote = {ABSTRACT 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 intergalactic medium (IGM). In this paper, we present a novel machine learning approach, using artificial neural networks, to reconstruct quasar continua around Lyα. Our Quasar Spectra from Artificial Neural Network based predictive Regression Algorithm(QSANNdRA) improves the error in this reconstruction compared to the state-of-the-art principal component analysis (PCA) based model in the literature by 14.2 per cent on average, and provides an improvement of 6.1 per cent on average when compared to an extension thereof. In comparison with the extended PCA model, QSANNdRA further achieves an improvement of 22.1 per cent and 16.8 per cent 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 $\bar{x}_\mathrm{H\, \small{I}} = 0.25^{+0.05}_{-0.05}$ at z = 7.0851 and $\bar{x}_\mathrm{H\, \small{I}} = 0.60^{+0.11}_{-0.11}$ at z = 7.5413. Our results are consistent with the literature and favour a rapid end to reionization.},
doi = {10.1093/mnras/staa505},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 3,
volume = 493,
place = {United Kingdom},
year = {2020},
month = {2}
}

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DOI: 10.1093/mnras/staa505

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