TITLE: The GIGANTES dataset: precision cosmology from voids in the machine learning era 

ABSTRACT: We present GIGANTES, the most extensive and realistic void catalog suite ever
released -- containing over 1 billion cosmic voids covering a volume larger
than the observable Universe, more than 20 TB of data, and created by running
the void finder VIDE on QUIJOTE's halo simulations. The expansive and detailed
GIGANTES suite, spanning thousands of cosmological models, opens up the study
of voids, answering compelling questions: Do voids carry unique cosmological
information? How is this information correlated with galaxy information?
Leveraging the large number of voids in the GIGANTES suite, our Fisher
constraints demonstrate voids contain additional information, critically
tightening constraints on cosmological parameters. We use traditional void
summary statistics (void size function, void density profile) and the void
auto-correlation function, which independently yields an error of
$0.13\,\mathrm{eV}$ on $\sum\,m_{\nu}$ for a 1 $h^{-3}\mathrm{Gpc}^3$
simulation, without CMB priors. Combining halos and voids we forecast an error
of $0.09\,\mathrm{eV}$ from the same volume. Extrapolating to next generation
multi-Gpc$^3$ surveys such as DESI, Euclid, SPHEREx, and the Roman Space
Telescope, we expect voids should yield an independent determination of
neutrino mass. Crucially, GIGANTES is the first void catalog suite expressly
built for intensive machine learning exploration. We illustrate this by
training a neural network to perform likelihood-free inference on the void size
function. Cosmology problems provide an impetus to develop novel deep learning
techniques, leveraging the symmetries embedded throughout the universe from
physical laws, interpreting models, and accurately predicting errors. With
GIGANTES, machine learning gains an impressive dataset, offering unique
problems that will stimulate new techniques.
We present GIGANTES, the most extensive and realistic void catalog suite ever
released -- containing over 1 billion cosmic voids covering a volume larger
than the observable Universe, more than 20 TB of data, and created by running
the void finder VIDE on QUIJOTE's halo simulations. The expansive and detailed
GIGANTES suite, spanning thousands of cosmological models, opens up the study
of voids, answering compelling questions: Do voids carry unique cosmological
information? How is this information correlated with galaxy information?
Leveraging the large number of voids in the GIGANTES suite, our Fisher
constraints demonstrate voids contain additional information, critically
tightening constraints on cosmological parameters. We use traditional void
summary statistics (void size function, void density profile) and the void
auto-correlation function, which independently yields an error of
$0.13\,\mathrm{eV}$ on $\sum\,m_{\nu}$ for a 1 $h^{-3}\mathrm{Gpc}^3$
simulation, without CMB priors. Combining halos and voids we forecast an error
of $0.09\,\mathrm{eV}$ from the same volume. Extrapolating to next generation
multi-Gpc$^3$ surveys such as DESI, Euclid, SPHEREx, and the Roman Space
Telescope, we expect voids should yield an independent determination of
neutrino mass. Crucially, GIGANTES is the first void catalog suite expressly
built for intensive machine learning exploration. We illustrate this by
training a neural network to perform likelihood-free inference on the void size
function. Cosmology problems provide an impetus to develop novel deep learning
techniques, leveraging the symmetries embedded throughout the universe from
physical laws, interpreting models, and accurately predicting errors. With
GIGANTES, machine learning gains an impressive dataset, offering unique
problems that will stimulate new techniques.
The GIGANTES dataset: precision cosmology from voids in the machine learning era
http://arxiv.org/abs/2107.02304v2