1,166 research outputs found
Bayesian deep learning for cosmic volumes with modified gravity
The new generation of galaxy surveys will provide unprecedented data allowing
us to test gravity at cosmological scales. A robust cosmological analysis of
the large-scale structure demands exploiting the nonlinear information encoded
in the cosmic web. Machine Learning techniques provide such tools, however, do
not provide a priori assessment of uncertainties. This study aims at extracting
cosmological parameters from modified gravity (MG) simulations through deep
neural networks endowed with uncertainty estimations. We implement Bayesian
neural networks (BNNs) with an enriched approximate posterior distribution
considering two cases: one with a single Bayesian last layer (BLL), and another
one with Bayesian layers at all levels (FullB). We train both BNNs with
real-space density fields and power-spectra from a suite of 2000 dark matter
only particle mesh -body simulations including modified gravity models
relying on MG-PICOLA covering 256 Mpc side cubical volumes with
128 particles. BNNs excel in accurately predicting parameters for
and and their respective correlation with the MG
parameter. We find out that BNNs yield well-calibrated uncertainty estimates
overcoming the over- and under-estimation issues in traditional neural
networks. We observe that the presence of MG parameter leads to a significant
degeneracy with being one of the possible explanations of the poor
MG predictions. Ignoring MG, we obtain a deviation of the relative errors in
and by at least . Moreover, we report consistent
results from the density field and power spectra analysis, and comparable
results between BLL and FullB experiments which permits us to save computing
time by a factor of two. This work contributes in setting the path to extract
cosmological parameters from complete small cosmic volumes towards the highly
nonlinear regime.Comment: 13 pages, 7 figures and 7 table
The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: RSD measurement from the power spectrum and bispectrum of the DR12 BOSS galaxies
We measure and analyse the bispectrum of the final, Data Release 12, galaxy
sample provided by the Baryon Oscillation Spectroscopic Survey, splitting by
selection algorithm into LOWZ and CMASS galaxies. The LOWZ sample contains
361\,762 galaxies with an effective redshift of , and the
CMASS sample 777\,202 galaxies with an effective redshift of . Combining the power spectrum, measured relative to the
line-of-sight, with the spherically averaged bispectrum, we are able to
constrain the product of the growth of structure parameter, , and the
amplitude of dark matter density fluctuations, , along with the
geometric Alcock-Paczynski parameters, the product of the Hubble constant and
the comoving sound horizon at the baryon drag epoch, , and the
angular distance parameter divided by the sound horizon, .
After combining pre-reconstruction RSD analyses of the power spectrum monopole,
quadrupole and bispectrum monopole; with post-reconstruction analysis of the
BAO power spectrum monopole and quadrupole, we find , , for
the LOWZ sample, and ,
, for the CMASS sample. We
find general agreement with previous BOSS DR11 and DR12 measurements. Combining
our dataset with {\it Planck15} we perform a null test of General Relativity
(GR) through the -parametrisation finding
, which is away from the GR
predictions.Comment: 34 pages, 22 figures, 8 tables. Accepted for publication in MNRAS.
Data available at https://sdss3.org//science/boss_publications.ph
Detection of Baryon Acoustic Oscillation Features in the Large-Scale 3-Point Correlation Function of SDSS BOSS DR12 CMASS Galaxies
We present the large-scale 3-point correlation function (3PCF) of the SDSS
DR12 CMASS sample of Luminous Red Galaxies, the largest-ever sample
used for a 3PCF or bispectrum measurement. We make the first high-significance
() detection of Baryon Acoustic Oscillations (BAO) in the 3PCF.
Using these acoustic features in the 3PCF as a standard ruler, we measure the
distance to to precision (statistical plus systematic). We
find for our
fiducial cosmology (consistent with Planck 2015) and bias model. This
measurement extends the use of the BAO technique from the 2-point correlation
function (2PCF) and power spectrum to the 3PCF and opens an avenue for deriving
additional cosmological distance information from future large-scale structure
redshift surveys such as DESI. Our measured distance scale from the 3PCF is
fairly independent from that derived from the pre-reconstruction 2PCF and is
equivalent to increasing the length of BOSS by roughly 10\%; reconstruction
appears to lower the independence of the distance measurements. Fitting a model
including tidal tensor bias yields a moderate significance (
detection of this bias with a value in agreement with the prediction from local
Lagrangian biasing.Comment: 15 pages, 7 figures, submitted MNRA
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