Reionization history constraints from neural network based predictions of high-redshift quasar continua. (arXiv:1912.01050v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Durovcikova_D/0/1/0/all/0/1">D. Ďurovčíková</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Katz_H/0/1/0/all/0/1">H. Katz</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bosman_S/0/1/0/all/0/1">S. E. I. Bosman</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Davies_F/0/1/0/all/0/1">F. B. Davies</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Devriendt_J/0/1/0/all/0/1">J. Devriendt</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Slyz_A/0/1/0/all/0/1">A. Slyz</a>
Observations of the early Universe suggest that reionization was complete by
$zsim6$, 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$alpha$ 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$alpha$, 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$alpha$. 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 $bar{x}_mathrm{HI} = 0.25^{+0.05}_{-0.05}$ at
$z=7.0851$ and $bar{x}_mathrm{HI} = 0.60^{+0.11}_{-0.11}$ at $z=7.5413$. Our
results are consistent with the literature and favour a rapid end to
reionization.
Observations of the early Universe suggest that reionization was complete by
$zsim6$, 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$alpha$ 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$alpha$, 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$alpha$. 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 $bar{x}_mathrm{HI} = 0.25^{+0.05}_{-0.05}$ at
$z=7.0851$ and $bar{x}_mathrm{HI} = 0.60^{+0.11}_{-0.11}$ at $z=7.5413$. Our
results are consistent with the literature and favour a rapid end to
reionization.
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