18 research outputs found
The bias of cosmic voids in the presence of massive neutrinos
Cosmic voids offer an extraordinary opportunity to study the effects of
massive neutrinos on cosmological scales. Because they are freely streaming,
neutrinos can penetrate the interior of voids more easily than cold dark matter
or baryons, which makes their relative contribution to the mass budget in voids
much higher than elsewhere in the Universe. In simulations it has recently been
shown how various characteristics of voids in the matter distribution are
affected by neutrinos, such as their abundance, density profiles, dynamics, and
clustering properties. However, the tracers used to identify voids in
observations (e.g., galaxies or halos) are affected by neutrinos as well, and
isolating the unique neutrino signatures inherent to voids becomes more
difficult. In this paper we make use of the DEMNUni suite of simulations to
investigate the clustering bias of voids in Fourier space as a function of
their core density and compensation. We find a clear dependence on the sum of
neutrino masses that remains significant even for void statistics extracted
from halos. In particular, we observe that the amplitude of the linear void
bias increases with neutrino mass for voids defined in dark matter, whereas
this trend gets reversed and slightly attenuated when measuring the relative
void-halo bias using voids identified in the halo distribution. Finally, we
argue how the original behaviour can be restored when considering observations
of the total matter distribution (e.g. via weak lensing), and comment on
scale-dependent effects in the void bias that may provide additional
information on neutrinos in the future.Comment: 23 pages, 18 figure
The GIGANTES dataset: precision cosmology from voids in the machine learning era
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
on for a 1
simulation, without CMB priors. Combining halos and voids we forecast an error
of from the same volume. Extrapolating to next generation
multi-Gpc 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.Comment: references added, typos corrected, version submitted to Ap
Cosmic voids::a novel probe to shed light on our Universe
Cosmic voids, the less dense patches of the Universe, are promising laboratories to extract cosmological information. Thanks to their unique low density character, voids are extremely sensitive to diffuse components such as neutrinos and dark energy, and represent ideal environments to study modifications of gravity, where the effects of such modifications are expected to be more prominent. Robust void-related observables, including for example redshift-space distortions (RSD) and weak lensing around voids, are a promising way to chase and test new physics. Cosmological analysis of the large-scale structure of the Universe predominantly relies on the high density regions. Current and upcoming surveys are designed to optimize the extraction of cosmological information from these zones, but leave voids under-exploited. A dense, large area spectroscopic survey with imaging capabilities is ideal to exploit the power of voids fully. Besides helping illuminate the nature of dark energy, modified gravity, and neutrinos, this survey will give access to a detailed map of under-dense regions, providing an unprecedented opportunity to observe and study a so far under-explored galaxy population
The quijote simulations
The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the hyperplane. At a single redshift, the simulations contain more than 8.5 trillion particles over a combined volume of 44,100 each simulation follows the evolution of 2563, 5123, or 10243 particles in a box of 1 h -1 Gpc length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: (1) to quantify the information content on cosmological observables and (2) to provide enough data to train machine-learning algorithms. In this paper, we describe the simulations and show a few of their applications. We also release the petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts; halo and void catalogs; and millions of summary statistics, such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions
Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) Along Track Oversampled (ATO) Observations
Mentor: Raymond E. Arvidson
From the Washington University Undergraduate Research Digest: WUURD, Volume 7, Issue 2, Spring 2012. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Kristin Sobotka, Editor
The Significance of Void Shape: Neutrino Mass from Voronoi Void-Halos?
International audienceMassive neutrinos suppress the growth of cosmic structure on nonlinear scales, motivating the use of information beyond the power spectrum to tighten constraints on the neutrino mass, for example by considering cosmic voids. It was recently proposed that constraints on neutrino mass from the halo mass function (HMF) can be improved by considering only the halos that reside within voids -- the void-halo mass function (VHMF). We extend this analysis, which made spherical assumptions about the shape of voids, to take into account the non-spherical nature of voids as defined by the Voronoi-tessellation-based void finder, VIDE. In turn, after accounting for one spurious non-spherical void, we find no evidence that the VHMF contains information beyond the HMF. Given this finding, we then introduce a novel summary statistic by splitting halos according to the emptiness of their individual environments, defined by the Voronoi cell volume each halo resides in, and combining the mass functions from each split. We name the corresponding statistic the VorHMF and find that it could provide information regarding neutrino mass beyond the HMF. Our work thus motivates the importance of accounting for the full shape of voids in future analyses, both in terms of removing outliers to achieve robust results and as an additional source of cosmological information