Foreground separation methods for satellite observations of the cosmic microwave background

A maximum entropy method (MEM) is presented for separating the emission due to different foreground components from simulated satellite observations of the cosmic microwave background radiation (CMBR). In particular, the method is applied to simulated observations by the proposed Planck Surveyor satellite. The simulations, performed by Bouchet and Gispert (1998), include emission from the CMBR, the kinetic and thermal Sunyaev-Zel'dovich (SZ) effects from galaxy clusters, as well as Galactic dust, free-free and synchrotron emission. We find that the MEM technique performs well and produces faithful reconstructions of the main input components. The method is also compared with traditional Wiener filtering and is shown to produce consistently better results, particularly in the recovery of the thermal SZ effect.Comment: 31 pages, 19 figures (bitmapped), accpeted for publication in MNRA

Novel quantum initial conditions for inflation

We present a novel approach for setting initial conditions on the mode functions of the Mukhanov Sazaki equation. These conditions are motivated by minimisation of the renormalised stress-energy tensor, and are valid for setting a vacuum state even in a context where the spacetime is changing rapidly. Moreover, these alternative conditions are potentially observationally distinguishable. We apply this to the kinetically dominated universe, and compare with the more traditional approach.Science and Technology Facilities CouncilThis is the author accepted manuscript. The final version is available from the American Physical Society via http://dx.doi.org/10.1103/PhysRevD.94.02404

AMI SZ observation of galaxy-cluster merger CIZA J2242+5301: Perpendicular flows of gas and dark matter

© 2018 The Author(s). Arcminute Microkelvin Imager observations towards CIZA J2242+5301, in comparison with observations of weak gravitational lensing and X-ray emission from the literature, are used to investigate the behaviour of non-baryonic dark matter (NBDM) and gas during the merger. Analysis of the Sunyaev-Zel'dovich (SZ) signal indicates the presence of high pressure gas elongated perpendicularly to the X-ray and weak-lensing morphologies, which, given the merger-axis constraints in the literature, implies that high pressure gas is pushed out into a linear structure during core passing. Simulations in the literature closely matching the inferred merger scenario show the formation of gas density and temperature structures perpendicular to the merger axis. These SZ observations are challenging for modified gravity theories in which NBDM is not the dominant contributor to galaxy-cluster gravity.STFC (ST/M001172/1) STFC (ST/K00333X/1) STFC (ST/M007065/1) STFC (ST/H008586/1) STFC (ST/J005673/1

Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism

Bayesian inference applied to microseismic activity monitoring allows the accurate location of microseismic events from recorded seismograms and the estimation of the associated uncertainties. However, the forward modelling of these microseismic events, which is necessary to perform Bayesian source inversion, can be prohibitively expensive in terms of computational resources. A viable solution is to train a surrogate model based on machine learning techniques, to emulate the forward model and thus accelerate Bayesian inference. In this paper, we substantially enhance previous work, which considered only sources with isotropic moment tensors. We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows complete and fast event locations for any source mechanism. Moreover, we show that our approach is computationally inexpensive, as it can be run in less than 1 hour on a commercial laptop, while yielding accurate results using less than 104 training seismograms. We additionally demonstrate how the trained emulators can be used to identify the source mechanism through the estimation of the Bayesian evidence. Finally, we demonstrate that our approach is robust to real noise as measured in field data. This work lays the foundations for efficient, accurate future joint determinations of event location and moment tensor, and associated uncertainties, which are ultimately key for accurately characterising human-induced and natural earthquakes, and for enhanced quantitative seismic hazard assessments

AMI observations of 10 CLASH galaxy clusters: SZ and X-ray data used together to determine cluster dynamical states

© 2016 The Authors. Published by Oxford University Press on behalf of The Royal Astronomical Society.Using Arcminute Microkelvin Imager (AMI) Sunyaev-Zel'dovich (SZ) observations towards 10 CLASH (Cluster Lensing and Supernova Survey with Hubble) clusters, we investigate the influence of cluster mergers on observational galaxy cluster studies. Although selected to be largely relaxed, there is disagreement in the literature on the dynamical states of CLASH sample members. We analyse ourAMIdata in a fully Bayesianway to produce estimated cluster parameters and consider the intrinsic correlations in our Navarro, Frenk and White/generalized Navarro, Frenk and White-based model. Varying pressure profile shape parameters, illustrating an influence of mergers on scaling relations, induces small deviations from the canonical selfsimilar predictions - in agreement with simulations of Poole et al. (2007) who found that merger activity causes only small scatter perpendicular to the relations. We demonstrate this effect observationally using the different dependences of SZ and X-ray signals to ne that cause different sensitivities to the shocking and/or fractionation produced by mergers. Plotting YX-Mgas relations (where YX = MgasT) derived from AMI SZ and from Chandra X-ray gives ratios of AMI and Chandra YX and Mgas estimates that indicate movement of clusters along the scaling relation, as predicted by Poole et al. (2007). Clusters that have moved most along the relation have the most discrepant TSZ and TX estimates: all the other clusters (apart from one) have SZ and X-ray estimates of Mgas, T and YX that agree within r500. We use SZ versus X-ray discrepancies in conjunction with Chandra maps and TX profiles, making comparisons with simulated cluster merger maps in Poole et al. (2006) to identify disturbed members of our sample and estimate merger stages.We thank the staff of the Mullard Radio Astronomy Observatory for their invaluable assistance in the commissioning and operation of AMI, which is supported by Cambridge University. WJH and CR are grateful for the support of STFC Studentships. CR also acknowledges the support of Cambridge University. MO and YCP acknowledge support from Research Fellowships from Sidney Sussex College and Trinity College, Cambridge, respectively. We thank Arif Babul for his assistance in accessing the Poole et al. online materials. Much of this work was undertaken on the COSMOS Shared Memory system at DAMTP, Cambridge University, operated on behalf of the STFC DiRAC HPC Facility. This equipment is funded by BIS National E-infrastructure capital grant ST/J005673/1 and STFC grants ST/H008586/1, ST/K00333X/1

Analysis of cosmic microwave background data on an incomplete sky

Measurement of the angular power spectrum of the cosmic microwave background is most often based on a spherical harmonic analysis of the observed temperature anisotropies. Even if all-sky maps are obtained, however, it is likely that the region around the Galactic plane will have to be removed as a result of its strong microwave emissions. The spherical harmonics are not orthogonal on the cut sky, but an orthonormal basis set can be constructed from a linear combination of the original functions. Previous implementations of this technique, based on Gram–Schmidt orthogonalization, were limited to maximum Legendre multipoles of lmax & 50, as they required all the modes have appreciable support on the cut-sky, whereas for large lmax the fraction of modes supported is equal to the fractional area of the region retained. This problem is solved by using a singular value decomposition to remove the poorly supported basis functions, although the treatment of the non-cosmological monopole and dipole modes necessarily becomes more complicated. A further difficulty is posed by computational limitations – orthogonalization for a general cut requires Oðl 6 maxÞ operations and Oðl 4 maxÞ storage and so is impractical for lmax * 200 at present. These problems are circumvented for the special case of constant (Galactic) latitude cuts, for which the storage requirements scale as Oðl 2 maxÞ and the operations count scales as Oðl 4 maxÞ. Less clear, however, is the stage of the data analysis at which the cut is best applied. As convolution is ill-defined on the incomplete sphere, beam-deconvolution should not be performed after the cut and, if all-sky component separation is as successful as simulations indicate, the Galactic plane should probably be removed immediately prior to power spectrum estimation

Surrogate regression modelling for fast seismogram generation and detection of microseismic events in heterogeneous velocity models

This is the author accepted manuscript. The final version is available from Oxford University Press (OUP) via the DOI in this record.Given a 3D heterogeneous velocity model with a few million voxels, fast generation of accurate seismic responses at specified receiver positions from known microseismic event locations is a well-known challenge in geophysics, since it typically involves numerical solution of the computationally expensive elastic wave equation. Thousands of such forward simulations are often a routine requirement for parameter estimation of microseimsic events via a suitable source inversion process. Parameter estimation based on forward modelling is often advantageous over a direct regression-based inversion approach when there are unknown number of parameters to be estimated and the seismic data has complicated noise characteristics which may not always allow a stable and unique solution in a direct inversion process. In this paper, starting from Graphics Processing Unit (GPU) based synthetic simulations of a few thousand forward seismic shots due to microseismic events via pseudo-spectral solution of elastic wave equation, we develop a step-by-step process to generate a surrogate regression modelling framework, using machine learning techniques that can produce accurate seismograms at specified receiver locations. The trained surrogate models can then be used as a high-speed meta-model/emulator or proxy for the original full elastic wave propagator to generate seismic responses for other microseismic event locations also. The accuracies of the surrogate models have been evaluated using two independent sets of training and testing Latin hypercube (LH) quasi-random samples, drawn from a heterogeneous marine velocity model. The predicted seismograms have been used thereafter to calculate batch likelihood functions, with specified noise characteristics. Finally, the trained models on 23 receivers placed at the sea-bed in a marine velocity model are used to determine the maximum likelihood estimate (MLE) of the event locations which can in future be used in a Bayesian analysis for microseismic event detection.This work has been supported by the Shell Projects and Technology. The Wilkes high performance GPU computing service at the University of Cambridge has been used in this work

Towards a framework for testing general relativity with extreme-mass-ratio-inspiral observations

Extreme-mass-ratio-inspiral observations from future space-based gravitational-wave detectors such as LISA will enable strong-field tests of general relativity with unprecedented precision, but at prohibitive computational cost if existing statistical techniques are used. In one such test that is currently employed for LIGO black hole binary mergers, generic deviations from relativity are represented by N deformation parameters in a generalized waveform model; the Bayesian evidence for each of its 2N combinatorial submodels is then combined into a posterior odds ratio for modified gravity over relativity in a null-hypothesis test. We adapt and apply this test to a generalized model for extreme-mass-ratio inspirals constructed on deformed black hole spacetimes, and focus our investigation on how computational efficiency can be increased through an evidence-free method of model selection. This method is akin to the algorithm known as product-space Markov chain Monte Carlo, but uses nested sampling and improved error estimates from a rethreading technique. We perform benchmarking and robustness checks for the method, and find order-of-magnitude computational gains over regular nested sampling in the case of synthetic data generated from the null model.AJKC acknowledges support from the Jet Propulsion Laboratory (JPL) Research and Technology Development programme. SH thanks the Science and Technology Facilities Council (STFC) for financial support. CJM acknowledges financial support provided under the European Union’s H2020 ERC Consolidator Grant ‘Matter and strong-field gravity: New frontiers in Einstein’s theory’ grant agreement no. MaGRaTh646597, and networking support by the COST Action CA16104. Parts of this work were performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from STFC. Parts of this work were also undertaken on the COSMOS Shared Memory system at DAMTP, University of Cambridge operated on behalf of the STFC DiRAC HPC Facility; this equipment is funded by BIS National E-infrastructure capital grant ST/J005673/1 and STFC grants ST/H008586/1, ST/K00333X/1. Parts of this work were also carried out at JPL, California Institute of Technology, under a contract with the National Aeronautics and Space Administration