Mapping Lyman-alpha forest three-dimensional large scale structure in real and redshift space. (arXiv:2107.07917v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Sinigaglia_F/0/1/0/all/0/1">Francesco Sinigaglia</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kitaura_F/0/1/0/all/0/1">Francisco-Shu Kitaura</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Balaguera_Antolinez_A/0/1/0/all/0/1">Andrés Balaguera-Antolínez</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Shimizu_I/0/1/0/all/0/1">Ikkoh Shimizu</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Nagamine_K/0/1/0/all/0/1">Kentaro Nagamine</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sanchez_Benavente_M/0/1/0/all/0/1">Manuel Sánchez-Benavente</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ata_M/0/1/0/all/0/1">Metin Ata</a>
This work presents a new physically-motivated supervised machine learning
method, Hydro-BAM, to reproduce the three-dimensional Lyman-$alpha$ forest
field in real and in redshift space learning from a reference hydrodynamic
simulation, thereby saving about 7 orders of magnitude in computing time. We
show that our method is accurate up to $ksim1,h,rm{Mpc}^{-1}$ in the one-
(PDF), two- (power-spectra) and three-point (bi-spectra) statistics of the
reconstructed fields. When compared to the reference simulation including
redshift space distortions, our method achieves deviations of $lesssim2%$ up
to $k=0.6,h,rm{Mpc}^{-1}$ in the monopole, $lesssim5%$ up to
$k=0.9,h,rm{Mpc}^{-1}$ in the quadrupole. The bi-spectrum is well reproduced
for triangle configurations with sides up to $k=0.8,h,rm{Mpc}^{-1}$. In
contrast, the commonly-adopted Fluctuating Gunn-Peterson approximation shows
significant deviations already neglecting peculiar motions at configurations
with sides of $k=0.2-0.4,h,rm{Mpc}^{-1}$ in the bi-spectrum, being also
significantly less accurate in the power-spectrum (within 5$%$ up to
$k=0.7,h,rm{Mpc}^{-1}$). We conclude that an accurate analysis of the
Lyman-$alpha$ forest requires considering the complex baryonic thermodynamical
large-scale structure relations. Our hierarchical domain specific machine
learning method can efficiently exploit this and is ready to generate accurate
Lyman-$alpha$ forest mock catalogues covering large volumes required by
surveys such as DESI and WEAVE.
This work presents a new physically-motivated supervised machine learning
method, Hydro-BAM, to reproduce the three-dimensional Lyman-$alpha$ forest
field in real and in redshift space learning from a reference hydrodynamic
simulation, thereby saving about 7 orders of magnitude in computing time. We
show that our method is accurate up to $ksim1,h,rm{Mpc}^{-1}$ in the one-
(PDF), two- (power-spectra) and three-point (bi-spectra) statistics of the
reconstructed fields. When compared to the reference simulation including
redshift space distortions, our method achieves deviations of $lesssim2%$ up
to $k=0.6,h,rm{Mpc}^{-1}$ in the monopole, $lesssim5%$ up to
$k=0.9,h,rm{Mpc}^{-1}$ in the quadrupole. The bi-spectrum is well reproduced
for triangle configurations with sides up to $k=0.8,h,rm{Mpc}^{-1}$. In
contrast, the commonly-adopted Fluctuating Gunn-Peterson approximation shows
significant deviations already neglecting peculiar motions at configurations
with sides of $k=0.2-0.4,h,rm{Mpc}^{-1}$ in the bi-spectrum, being also
significantly less accurate in the power-spectrum (within 5$%$ up to
$k=0.7,h,rm{Mpc}^{-1}$). We conclude that an accurate analysis of the
Lyman-$alpha$ forest requires considering the complex baryonic thermodynamical
large-scale structure relations. Our hierarchical domain specific machine
learning method can efficiently exploit this and is ready to generate accurate
Lyman-$alpha$ forest mock catalogues covering large volumes required by
surveys such as DESI and WEAVE.
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