6,419 research outputs found
Revisiting the holographic dark energy in a non-flat universe: alternative model and cosmological parameter constraints
We propose an alternative model for the holographic dark energy in a non-flat
universe. This new model differs from the previous one in that the IR length
cutoff is taken to be exactly the event horizon size in a non-flat
universe, which is more natural and theoretically/conceptually concordant with
the model of holographic dark energy in a flat universe. We constrain the model
using the recent observational data including the type Ia supernova data from
SNLS3, the baryon acoustic oscillation data from 6dF, SDSS-DR7, BOSS-DR11, and
WiggleZ, the cosmic microwave background data from Planck, and the Hubble
constant measurement from HST. In particular, since some previous studies have
shown that the color-luminosity parameter of supernovae is likely to
vary during the cosmic evolution, we also consider such a case that in
SNLS3 is time-varying in our data fitting. Compared to the constant
case, the time-varying case reduces the value of by about 35
and results in that deviates from a constant at about 5 level,
well consistent with the previous studies. For the parameter of the
holographic dark energy, the constant fit gives and
the time-varying fit yields . In addition, an open
universe is favored (at about 2) for the model by the current data.Comment: 8 pages, 4 figure
Unfolding Hidden Barriers by Active Enhanced Sampling
Collective variable (CV) or order parameter based enhanced sampling
algorithms have achieved great success due to their ability to efficiently
explore the rough potential energy landscapes of complex systems. However, the
degeneracy of microscopic configurations, originating from the orthogonal space
perpendicular to the CVs, is likely to shadow "hidden barriers" and greatly
reduce the efficiency of CV-based sampling. Here we demonstrate that systematic
machine learning CV, through enhanced sampling, can iteratively lift such
degeneracies on the fly. We introduce an active learning scheme that consists
of a parametric CV learner based on deep neural network and a CV-based enhanced
sampler. Our active enhanced sampling (AES) algorithm is capable of identifying
the least informative regions based on a historical sample, forming a positive
feedback loop between the CV learner and sampler. This approach is able to
globally preserve kinetic characteristics by incrementally enhancing both
sample completeness and CV quality.Comment: 5 pages, 3 figure
Search for sterile neutrinos in holographic dark energy cosmology: Reconciling Planck observation with the local measurement of the Hubble constant
We search for sterile neutrinos in the holographic dark energy cosmology by
using the latest observational data. To perform the analysis, we employ the
current cosmological observations, including the cosmic microwave background
temperature power spectrum data from the Planck mission, the baryon acoustic
oscillation measurements, the type Ia supernova data, the redshift space
distortion measurements, the shear data of weak lensing observation, the Planck
lensing measurement, and the latest direct measurement of as well. We
show that, compared to the CDM cosmology, the holographic dark energy
cosmology with sterile neutrinos can relieve the tension between the Planck
observation and the direct measurement of much better. Once we include
the measurement in the global fit, we find that the hint of the existence
of sterile neutrinos in the holographic dark energy cosmology can be given.
Under the constraint of the all-data combination, we obtain and , indicating
that the detection of in the holographic dark energy
cosmology is at the level and the massless or very light sterile
neutrino is favored by the current observations.Comment: 10 pages, 4 figures; typos corrected, published in PR
Neutrinos in the holographic dark energy model: constraints from latest measurements of expansion history and growth of structure
The model of holographic dark energy (HDE) with massive neutrinos and/or dark
radiation is investigated in detail. The background and perturbation evolutions
in the HDE model are calculated. We employ the PPF approach to overcome the
gravity instability difficulty (perturbation divergence of dark energy) led by
the equation-of-state parameter evolving across the phantom divide
in the HDE model with . We thus derive the evolutions of density
perturbations of various components and metric fluctuations in the HDE model.
The impacts of massive neutrino and dark radiation on the CMB anisotropy power
spectrum and the matter power spectrum in the HDE scenario are discussed.
Furthermore, we constrain the models of HDE with massive neutrinos and/or dark
radiation by using the latest measurements of expansion history and growth of
structure, including the Planck CMB temperature data, the baryon acoustic
oscillation data, the JLA supernova data, the Hubble constant direct
measurement, the cosmic shear data of weak lensing, the Planck CMB lensing
data, and the redshift space distortions data. We find that
eV (95\% CL) and in the HDE model from the
constraints of these data.Comment: 18 pages, 5 figures; revised version accepted for publication in JCA
Wedge modules for two-parameter quantum groups
The Yang-Baxterization R(z) of the trigonometric R-matrix is computed for the
two-parameter quantum affine algebra of type A. Using the fusion procedure we
construct all fundamental representations of the quantum algebra as wedge
products of the natural representation.Comment: Updated versio
A Note on State Decomposition Independent Local Invariants
We derive a set of invariants under local unitary transformations for
arbitrary dimensional quantum systems. These invariants are given by
hyperdeterminants and independent from the detailed pure state decompositions
of a given quantum state. They also give rise to necessary conditions for the
equivalence of quantum states under local unitary transformations
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