133 research outputs found
A framework for testing isotropy with the cosmic microwave background
We present a new framework for testing the isotropy of the Universe using
cosmic microwave background data, building on the nested-sampling ANICOSMO
code. Uniquely, we are able to constrain the scalar, vector and tensor degrees
of freedom alike; previous studies only considered the vector mode (linked to
vorticity). We employ Bianchi type VII cosmologies to model the anisotropic
Universe, from which other types may be obtained by taking suitable limits. In
a separate development, we improve the statistical analysis by including the
effect of Bianchi power in the high-, as well as the low-,
likelihood. To understand the effect of all these changes, we apply our new
techniques to WMAP data. We find no evidence for anisotropy, constraining shear
in the vector mode to (95% CL). For the
first time, we place limits on the tensor mode; unlike other modes, the tensor
shear can grow from a near-isotropic early Universe. The limit on this type of
shear is (95% CL).Comment: 11 pages, 6 figures, v3: minor modifications to match version
accepted by MNRA
How isotropic is the Universe?
A fundamental assumption in the standard model of cosmology is that the
Universe is isotropic on large scales. Breaking this assumption leads to a set
of solutions to Einstein's field equations, known as Bianchi cosmologies, only
a subset of which have ever been tested against data. For the first time, we
consider all degrees of freedom in these solutions to conduct a general test of
isotropy using cosmic microwave background temperature and polarization data
from Planck. For the vector mode (associated with vorticity), we obtain a limit
on the anisotropic expansion of (95%
CI), which is an order of magnitude tighter than previous Planck results that
used CMB temperature only. We also place upper limits on other modes of
anisotropic expansion, with the weakest limit arising from the regular tensor
mode, (95% CI). Including all
degrees of freedom simultaneously for the first time, anisotropic expansion of
the Universe is strongly disfavoured, with odds of 121,000:1 against.Comment: 6 pages, 1 figure, v2: replaced with version accepted by PR
Hierarchical Bayesian Detection Algorithm for Early-Universe Relics in the Cosmic Microwave Background
A number of theoretically well-motivated additions to the standard
cosmological model predict weak signatures in the form of spatially localized
sources embedded in the cosmic microwave background (CMB) fluctuations. We
present a hierarchical Bayesian statistical formalism and a complete data
analysis pipeline for testing such scenarios. We derive an accurate
approximation to the full posterior probability distribution over the
parameters defining any theory that predicts sources embedded in the CMB, and
perform an extensive set of tests in order to establish its validity. The
approximation is implemented using a modular algorithm, designed to avoid a
posteriori selection effects, which combines a candidate-detection stage with a
full Bayesian model-selection and parameter-estimation analysis. We apply this
pipeline to theories that predict cosmic textures and bubble collisions,
extending previous analyses by using: (1) adaptive-resolution techniques,
allowing us to probe features of arbitrary size, and (2) optimal filters, which
provide the best possible sensitivity for detecting candidate signatures. We
conclude that the WMAP 7-year data do not favor the addition of either cosmic
textures or bubble collisions to the standard cosmological model, and place
robust constraints on the predicted number of such sources. The expected
numbers of bubble collisions and cosmic textures on the CMB sky within our
detection thresholds are constrained to be fewer than 4.0 and 5.2 at 95%
confidence, respectively.Comment: 34 pages, 18 figures. v3: corrected very minor typos to match
published versio
Learned Interferometric Imaging for the SPIDER Instrument
The Segmented Planar Imaging Detector for Electro-Optical Reconnaissance
(SPIDER) is an optical interferometric imaging device that aims to offer an
alternative to the large space telescope designs of today with reduced size,
weight and power consumption. This is achieved through interferometric imaging.
State-of-the-art methods for reconstructing images from interferometric
measurements adopt proximal optimization techniques, which are computationally
expensive and require handcrafted priors. In this work we present two
data-driven approaches for reconstructing images from measurements made by the
SPIDER instrument. These approaches use deep learning to learn prior
information from training data, increasing the reconstruction quality, and
significantly reducing the computation time required to recover images by
orders of magnitude. Reconstruction time is reduced to
milliseconds, opening up the possibility of real-time imaging with SPIDER for
the first time. Furthermore, we show that these methods can also be applied in
domains where training data is scarce, such as astronomical imaging, by
leveraging transfer learning from domains where plenty of training data are
available.Comment: 21 pages, 14 figure
Sparse Inpainting and Isotropy
Sparse inpainting techniques are gaining in popularity as a tool for
cosmological data analysis, in particular for handling data which present
masked regions and missing observations. We investigate here the relationship
between sparse inpainting techniques using the spherical harmonic basis as a
dictionary and the isotropy properties of cosmological maps, as for instance
those arising from cosmic microwave background (CMB) experiments. In
particular, we investigate the possibility that inpainted maps may exhibit
anisotropies in the behaviour of higher-order angular polyspectra. We provide
analytic computations and simulations of inpainted maps for a Gaussian
isotropic model of CMB data, suggesting that the resulting angular trispectrum
may exhibit small but non-negligible deviations from isotropy.Comment: 18 pages, 6 figures. v3: matches version published in JCAP;
formatting changes and single typo correction only. Code available from
http://zuserver2.star.ucl.ac.uk/~smf/code.htm
Sparse Bayesian mass-mapping using trans-dimensional MCMC
Uncertainty quantification is a crucial step of cosmological mass-mapping
that is often ignored. Suggested methods are typically only approximate or make
strong assumptions of Gaussianity of the shear field. Probabilistic sampling
methods, such as Markov chain Monte Carlo (MCMC), draw samples form a
probability distribution, allowing for full and flexible uncertainty
quantification, however these methods are notoriously slow and struggle in the
high-dimensional parameter spaces of imaging problems. In this work we use, for
the first time, a trans-dimensional MCMC sampler for mass-mapping, promoting
sparsity in a wavelet basis. This sampler gradually grows the parameter space
as required by the data, exploiting the extremely sparse nature of mass maps in
wavelet space. The wavelet coefficients are arranged in a tree-like structure,
which adds finer scale detail as the parameter space grows. We demonstrate the
trans-dimensional sampler on galaxy cluster-scale images where the planar
modelling approximation is valid. In high-resolution experiments, this method
produces naturally parsimonious solutions, requiring less than 1% of the
potential maximum number of wavelet coefficients and still producing a good fit
to the observed data. In the presence of noisy data, trans-dimensional MCMC
produces a better reconstruction of mass-maps than the standard smoothed
Kaiser-Squires method, with the addition that uncertainties are fully
quantified. This opens up the possibility for new mass maps and inferences
about the nature of dark matter using the new high-resolution data from
upcoming weak lensing surveys such as Euclid
Design & manufacture of a high-performance bicycle crank by additive manufacturing
A new practical workflow for the laser Powder Bed Fusion (PBF) process, incorporating topological design, mechanical simulation, manufacture, and validation by computed tomography is presented, uniquely applied to a consumer product (crank for a high-performance racing bicycle), an approach that is tangible and adoptable by industry. The lightweight crank design was realised using topology optimisation software, developing an optimal design iteratively from a simple primitive within a design space and with the addition of load boundary conditions (obtained from prior biomechanical crank force–angle models) and constraints. Parametric design modification was necessary to meet the Design for Additive Manufacturing (DfAM)considerations for PBF to reduce build time, material usage, and post-processing labour. Static testing proved performance close to current market leaders with the PBF manufactured crank found to be stiffer than the benchmark design (static load deflection of 7.0±0.5 mm c.f. 7.67mm for a Shimano crank at a competitive mass (155g vs. 175g). Dynamic mechanical performance proved inadequate, with failure at 2495±125cycles; the failure mechanism was consistent in both its form and location. This research is valuable and novel as it demonstrates a complete work flow from design, manufacture, post-treatment, and validation of a highly loaded PBF manufactured consumer component, offering practitioners a validated approach to the application of PBF for components with application outside of the accepted sectors (aerospace, biomedical, autosports, space, and power generation)
Wavelet-Bayesian inference of cosmic strings embedded in the cosmic microwave background
Cosmic strings are a well-motivated extension to the standard cosmological
model and could induce a subdominant component in the anisotropies of the
cosmic microwave background (CMB), in addition to the standard inflationary
component. The detection of strings, while observationally challenging, would
provide a direct probe of physics at very high energy scales. We develop a new
framework for cosmic string inference, constructing a Bayesian analysis in
wavelet space where the string-induced CMB component has distinct statistical
properties to the standard inflationary component. Our wavelet-Bayesian
framework provides a principled approach to compute the posterior distribution
of the string tension and the Bayesian evidence ratio comparing the
string model to the standard inflationary model. Furthermore, we present a
technique to recover an estimate of any string-induced CMB map embedded in
observational data. Using Planck-like simulations we demonstrate the
application of our framework and evaluate its performance. The method is
sensitive to for Nambu-Goto string simulations
that include an integrated Sachs-Wolfe (ISW) contribution only and do not
include any recombination effects, before any parameters of the analysis are
optimised. The sensitivity of the method compares favourably with other
techniques applied to the same simulations.Comment: 18 pages, 14 figures, minor changes to match version accepted by
MNRA
Fast emulation of anisotropies induced in the cosmic microwave background by cosmic strings
Cosmic strings are linear topological defects that may have been produced
during symmetry-breaking phase transitions in the very early Universe. In an
expanding Universe the existence of causally separate regions prevents such
symmetries from being broken uniformly, with a network of cosmic string
inevitably forming as a result. To faithfully generate observables of such
processes requires computationally expensive numerical simulations, which
prohibits many types of analyses. We propose a technique to instead rapidly
emulate observables, thus circumventing simulation. Emulation is a form of
generative modelling, often built upon a machine learning backbone. End-to-end
emulation often fails due to high dimensionality and insufficient training
data. Consequently, it is common to instead emulate a latent representation
from which observables may readily be synthesised. Wavelet phase harmonics are
an excellent latent representations for cosmological fields, both as a summary
statistic and for emulation, since they do not require training and are highly
sensitive to non-Gaussian information. Leveraging wavelet phase harmonics as a
latent representation, we develop techniques to emulate string induced CMB
anisotropies over a 7.2 degree field of view, with sub-arcminute resolution, in
under a minute on a single GPU. Beyond generating high fidelity emulations, we
provide a technique to ensure these observables are distributed correctly,
providing a more representative ensemble of samples. The statistics of our
emulations are commensurate with those calculated on comprehensive Nambu-Goto
simulations. Our findings indicate these fast emulation approaches may be
suitable for wide use in, e.g., simulation based inference pipelines. We make
our code available to the community so that researchers may rapidly emulate
cosmic string induced CMB anisotropies for their own analysis
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