1,664 research outputs found
Physics of dark energy particles
We consider the astrophysical and cosmological implications of the existence
of a minimum density and mass due to the presence of the cosmological constant.
If there is a minimum length in nature, then there is an absolute minimum mass
corresponding to a hypothetical particle with radius of the order of the Planck
length. On the other hand, quantum mechanical considerations suggest a
different minimum mass. These particles associated with the dark energy can be
interpreted as the ``quanta'' of the cosmological constant. We study the
possibility that these particles can form stable stellar-type configurations
through gravitational condensation, and their Jeans and Chandrasekhar masses
are estimated. From the requirement of the energetic stability of the minimum
density configuration on a macroscopic scale one obtains a mass of the order of
10^55 g, of the same order of magnitude as the mass of the universe. This mass
can also be interpreted as the Jeans mass of the dark energy fluid. Furthermore
we present a representation of the cosmological constant and of the total mass
of the universe in terms of `classical' fundamental constants.Comment: 10 pages, no figures; typos corrected, 4 references added; 1
reference added; reference added; entirely revised version, contains new
parts, now 14 page
Bounds on the basic physical parameters for anisotropic compact general relativistic objects
We derive upper and lower limits for the basic physical parameters
(mass-radius ratio, anisotropy, redshift and total energy) for arbitrary
anisotropic general relativistic matter distributions in the presence of a
cosmological constant. The values of these quantities are strongly dependent on
the value of the anisotropy parameter (the difference between the tangential
and radial pressure) at the surface of the star. In the presence of the
cosmological constant, a minimum mass configuration with given anisotropy does
exist. Anisotropic compact stellar type objects can be much more compact than
the isotropic ones, and their radii may be close to their corresponding
Schwarzschild radii. Upper bounds for the anisotropy parameter are also
obtained from the analysis of the curvature invariants. General restrictions
for the redshift and the total energy (including the gravitational
contribution) for anisotropic stars are obtained in terms of the anisotropy
parameter. Values of the surface redshift parameter greater than two could be
the main observational signature for anisotropic stellar type objects.Comment: 18 pages, no figures, accepted for publication in CQ
Regression with Linear Factored Functions
Many applications that use empirically estimated functions face a curse of
dimensionality, because the integrals over most function classes must be
approximated by sampling. This paper introduces a novel regression-algorithm
that learns linear factored functions (LFF). This class of functions has
structural properties that allow to analytically solve certain integrals and to
calculate point-wise products. Applications like belief propagation and
reinforcement learning can exploit these properties to break the curse and
speed up computation. We derive a regularized greedy optimization scheme, that
learns factored basis functions during training. The novel regression algorithm
performs competitively to Gaussian processes on benchmark tasks, and the
learned LFF functions are with 4-9 factored basis functions on average very
compact.Comment: Under review as conference paper at ECML/PKDD 201
Zero Energy of Plane-Waves for ELKOs
We consider the ELKO field in interaction through contorsion with its own
spin density, and we investigate the form of the consequent autointeractions;
to do so we take into account the high-density limit and find plane wave
solutions: such plane waves give rise to contorsional autointeractions for
which the Ricci metric curvature vanishes and therefore the energy density is
equal to zero identically. Consequences are discussed.Comment: 7 page
Minimum mass-radius ratio for charged gravitational objects
We rigorously prove that for compact charged general relativistic objects
there is a lower bound for the mass-radius ratio. This result follows from the
same Buchdahl type inequality for charged objects, which has been extensively
used for the proof of the existence of an upper bound for the mass-radius
ratio. The effect of the vacuum energy (a cosmological constant) on the minimum
mass is also taken into account. Several bounds on the total charge, mass and
the vacuum energy for compact charged objects are obtained from the study of
the Ricci scalar invariants. The total energy (including the gravitational one)
and the stability of the objects with minimum mass-radius ratio is also
considered, leading to a representation of the mass and radius of the charged
objects with minimum mass-radius ratio in terms of the charge and vacuum energy
only.Comment: 19 pages, accepted by GRG, references corrected and adde
Dark spinors with torsion in cosmology
We solve one of the open problems in Einstein-Cartan theory, namely we find a
natural matter source whose spin angular momentum tensor is compatible with the
cosmological principle. We analyze the resulting evolution equations and find
that an epoch of accelerated expansion is an attractor. The torsion field
quickly decays in that period. Our results are interpreted in the context of
the standard model of cosmology.Comment: 7 pages, 3 figures; reference added, minor improvement
Hedgehog Spin-vortex Crystal Antiferromagnetic Quantum Criticality in CaK(Fe1-xNix)4As4 Revealed by NMR
Two ordering states, antiferromagnetism and nematicity, have been observed in
most iron-based superconductors (SCs). In contrast to those SCs, the newly
discovered SC CaK(FeNi)As exhibits an antiferromagnetic
(AFM) state, called hedgehog spin-vortex crystal structure, without nematic
order, providing the opportunity for the investigation into the relationship
between spin fluctuations and SC without any effects of nematic fluctuations.
Our As nuclear magnetic resonance studies on
CaK(FeNi)As (0 0.049) revealed that
CaKFeAs is located close to a hidden hedgehog SVC AFM quantum-critical
point (QCP). The magnetic QCP without nematicity in
CaK(FeNi)As highlights the close connection of spin
fluctuations and superconductivity in iron-based SCs. The advantage of
stoichiometric composition also makes CaKFeAs an ideal platform for
further detailed investigation of the relationship between magnetic QCP and
superconductivity in iron-based SCs without disorder effects.Comment: 6 pages, 5 figures, accepted for publication in Phys. Rev. Let
The Role of Diverse Replay for Generalisation in Reinforcement Learning
In reinforcement learning (RL), key components of many algorithms are the
exploration strategy and replay buffer. These strategies regulate what
environment data is collected and trained on and have been extensively studied
in the RL literature. In this paper, we investigate the impact of these
components in the context of generalisation in multi-task RL. We investigate
the hypothesis that collecting and training on more diverse data from the
training environment will improve zero-shot generalisation to new
environments/tasks. We motivate mathematically and show empirically that
generalisation to states that are "reachable" during training is improved by
increasing the diversity of transitions in the replay buffer. Furthermore, we
show empirically that this same strategy also shows improvement for
generalisation to similar but "unreachable" states and could be due to improved
generalisation of latent representations.Comment: 14 pages, 8 figure
E-MCTS: Deep Exploration in Model-Based Reinforcement Learning by Planning with Epistemic Uncertainty
One of the most well-studied and highly performing planning approaches used
in Model-Based Reinforcement Learning (MBRL) is Monte-Carlo Tree Search (MCTS).
Key challenges of MCTS-based MBRL methods remain dedicated deep exploration and
reliability in the face of the unknown, and both challenges can be alleviated
through principled epistemic uncertainty estimation in the predictions of MCTS.
We present two main contributions: First, we develop methodology to propagate
epistemic uncertainty in MCTS, enabling agents to estimate the epistemic
uncertainty in their predictions. Second, we utilize the propagated uncertainty
for a novel deep exploration algorithm by explicitly planning to explore. We
incorporate our approach into variations of MCTS-based MBRL approaches with
learned and provided dynamics models, and empirically show deep exploration
through successful epistemic uncertainty estimation achieved by our approach.
We compare to a non-planning-based deep-exploration baseline, and demonstrate
that planning with epistemic MCTS significantly outperforms non-planning based
exploration in the investigated deep exploration benchmark.Comment: Submitted to NeurIPS 2023, accepted to EWRL 202
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