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David Parkinson

Publications and source records attributed to David Parkinson.

At least 91 records · Page 5Linked to original sources

The WiggleZ Dark Energy Survey: Cosmological neutrino mass constraint from blue high-redshift galaxies

The absolute neutrino mass scale is currently unknown, but can be constrained from cosmology. The WiggleZ high redshift star-forming blue galaxy sample is less sensitive to systematics from non-linear structure formation, redshift-space distortions and galaxy bias than previous surveys. We obtain a upper limit on the sum of neutrino masses of 0.60eV (95% confidence) for WiggleZ+Wilkinson Microwave Anisotropy Probe. Combining with priors on the Hubble Parameter and the baryon acoustic oscillation scale gives an upper limit of 0.29eV, which is the strongest neutrino mass constraint derived from spectroscopic galaxy redshift surveys.

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Optimizing future dark energy surveys for model selection goals

We demonstrate a methodology for optimizing the ability of future dark energy surveys to answer model selection questions, such as `Is acceleration due to a cosmological constant or a dynamical dark energy model?'. Model selection Figures of Merit are defined, exploiting the Bayes factor, and surveys optimized over their design parameter space via a Monte Carlo method. As a specific example we apply our methods to generic multi-fibre baryon acoustic oscillation spectroscopic surveys, comparable to that proposed for SuMIRe PFS, and present implementations based on the Savage-Dickey Density Ratio that are both accurate and practical for use in optimization. It is shown that whilst the optimal surveys using model selection agree with those found using the Dark Energy Task Force (DETF) Figure of Merit, they provide better informed flexibility of survey configuration and an absolute scale for performance; for example, we find survey configurations with close to optimal model selection performance despite their corresponding DETF Figure of Merit being at only 50% of its maximum. This Bayes factor approach allows us to interpret the survey configurations that will be good enough for the task at hand, vital especially when wanting to add extra science goals and in dealing with time restrictions or multiple probes within the same project.

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On the prior dependence of constraints on the tensor-to-scalar ratio

We investigate the prior dependence of constraints on cosmic tensor perturbations. Commonly imposed is the strong prior of the single-field inflationary consistency equation, relating the tensor spectral index nT to the tensor-to-scalar ratio r. Dropping it leads to significantly different constraints on nT, with both positive and negative values allowed with comparable likelihood, and substantially increases the upper limit on r on scales k = 0.01 Mpc^-1 to 0.05 Mpc^-1, by a factor of ten or more. Even if the consistency equation is adopted, a uniform prior on r on one scale does not correspond to a uniform one on another; constraints therefore depend on the pivot scale chosen. We assess the size of this effect and determine the optimal scale for constraining the tensor amplitude, both with and without the consistency relation.

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The WiggleZ Dark Energy Survey: mapping the distance-redshift relation with baryon acoustic oscillations

We present measurements of the baryon acoustic peak at redshifts z = 0.44, 0.6 and 0.73 in the galaxy correlation function of the final dataset of the WiggleZ Dark Energy Survey. We combine our correlation function with lower-redshift measurements from the 6-degree Field Galaxy Survey and Sloan Digital Sky Survey, producing a stacked survey correlation function in which the statistical significance of the detection of the baryon acoustic peak is 4.9-sigma relative to a zero-baryon model with no peak. We fit cosmological models to this combined baryon acoustic oscillation (BAO) dataset comprising six distance-redshift data points, and compare the results to similar fits to the latest compilation of supernovae (SNe) and Cosmic Microwave Background (CMB) data. The BAO and SNe datasets produce consistent measurements of the equation-of-state w of dark energy, when separately combined with the CMB, providing a powerful check for systematic errors in either of these distance probes. Combining all datasets we determine w = -1.03 +/- 0.08 for a flat Universe, consistent with a cosmological constant model. Assuming dark energy is a cosmological constant and varying the spatial curvature, we find Omega_k = -0.004 +/- 0.006.

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The WiggleZ Dark Energy Survey: constraining the evolution of Newton's constant using the growth rate of structure

We constrain the evolution of Newton's constant using the growth rate of large-scale structure measured by the WiggleZ Dark Energy Survey in the redshift range $0.1 < z < 0.9$. We use this data in two ways. Firstly we constrain the matter density of the Universe, $\omms$ (assuming General Relativity), and use this to construct a diagnostic to detect the presence of an evolving Newton's constant. Secondly we directly measure the evolution of Newton's constant, $\Geff$, that appears in Modified Gravity theories, without assuming General Relativity to be true. The novelty of these approaches are that, contrary to other methods, they do not require knowledge of the expansion history of the Universe, $H(z)$, making them model independent tests. Our constraints for the second derivative of Newton's constant at the present day, assuming it is slowly evolving as suggested by Big Bang Nucleosynthesis constraints, using the WiggleZ data is $\ddotGeff(t_0)=-1.19\pm 0.95\cdot 10^{-20}h^2 \textrm{yr}^{-2}$, where $h$ is defined via $H_0=100 h km s^{-1}Mpc^{-1}$, while using both the WiggleZ and the Sloan Digital Sky Survey Luminous Red Galaxy (SDSS LRG) data is $\ddotGeff(t_0)=-3.6\pm 6.8\cdot 10^{-21}h^2 \rm{yr}^{-2}$, both being consistent with General Relativity. Finally, our constraint for the rms mass fluctuation $σ_8$ using the WiggleZ data is $σ_8=0.75 \pm 0.08$, while using both the WiggleZ and the SDSS LRG data $σ_8=0.77 \pm 0.07$, both in good agreement with the latest measurements from the Cosmic Microwave Background radiation.

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The WiggleZ Dark Energy Survey: testing the cosmological model with baryon acoustic oscillations at z=0.6

We measure the imprint of baryon acoustic oscillations (BAOs) in the galaxy clustering pattern at the highest redshift achieved to date, z=0.6, using the distribution of N=132,509 emission-line galaxies in the WiggleZ Dark Energy Survey. We quantify BAOs using three statistics: the galaxy correlation function, power spectrum and the band-filtered estimator introduced by Xu et al. (2010). The results are mutually consistent, corresponding to a 4.0% measurement of the cosmic distance-redshift relation at z=0.6 (in terms of the acoustic parameter "A(z)" introduced by Eisenstein et al. (2005) we find A(z=0.6) = 0.452 +/- 0.018). Both BAOs and power spectrum shape information contribute toward these constraints. The statistical significance of the detection of the acoustic peak in the correlation function, relative to a wiggle-free model, is 3.2-sigma. The ratios of our distance measurements to those obtained using BAOs in the distribution of Luminous Red Galaxies at redshifts z=0.2 and z=0.35 are consistent with a flat Lambda Cold Dark Matter model that also provides a good fit to the pattern of observed fluctuations in the Cosmic Microwave Background (CMB) radiation. The addition of the current WiggleZ data results in a ~ 30% improvement in the measurement accuracy of a constant equation-of-state, w, using BAO data alone. Based solely on geometric BAO distance ratios, accelerating expansion (w < -1/3) is required with a probability of 99.8%, providing a consistency check of conclusions based on supernovae observations. Further improvements in cosmological constraints will result when the WiggleZ Survey dataset is complete.

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Application of Bayesian model averaging to measurements of the primordial power spectrum

Cosmological parameter uncertainties are often stated assuming a particular model, neglecting the model uncertainty, even when Bayesian model selection is unable to identify a conclusive best model. Bayesian model averaging is a method for assessing parameter uncertainties in situations where there is also uncertainty in the underlying model. We apply model averaging to the estimation of the parameters associated with the primordial power spectra of curvature and tensor perturbations. We use CosmoNest and MultiNest to compute the model Evidences and posteriors, using cosmic microwave data from WMAP, ACBAR, BOOMERanG and CBI, plus large-scale structure data from the SDSS DR7. We find that the model-averaged 95% credible interval for the spectral index using all of the data is 0.940 < n_s < 1.000, where n_s is specified at a pivot scale 0.015 Mpc^{-1}. For the tensors model averaging can tighten the credible upper limit, depending on prior assumptions.

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Results from the Supernova Photometric Classification Challenge

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN, and 9 entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe~Ia has an efficiency of 0.96 and an SN~Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer keys so that developers can evaluate their own analysis.

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Statistical Classification Techniques for Photometric Supernova Typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on lightcurves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20,000 simulated Dark Energy Survey lightcurves. We demonstrate that these methods are comparable to the best template fitting methods currently used, and in particular do not require the redshift of the host galaxy or candidate. However both methods require a training sample that is representative of the full population, so typical spectroscopic supernova subsamples will lead to poor performance. To enable the full potential of such blind methods, we recommend that representative training samples should be used and so specific attention should be given to their creation in the design phase of future photometric surveys.

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Unified dark energy and dark matter from a scalar field different from quintessence

We explore unification of dark matter and dark energy in a theory containing a scalar field of non-Lagrangian type, obtained by direct insertion of a kinetic term into the energy-momentum tensor. This scalar is different from quintessence, having an equation of state between -1 and 0 and a zero sound speed in its rest frame. We solve the equations of motion for an exponential potential via a rewriting as an autonomous system, and demonstrate the observational viability of the scenario, for sufficiently small exponential potential parameter λ, by comparison to a compilation of kinematical cosmological data.

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Constraining the dark fluid

Cosmological observations are normally fit under the assumption that the dark sector can be decomposed into dark matter and dark energy components. However, as long as the probes remain purely gravitational, there is no unique decomposition and observations can only constrain a single dark fluid; this is known as the dark degeneracy. We use observations to directly constrain this dark fluid in a model-independent way, demonstrating in particular that the data cannot be fit by a dark fluid with a single constant equation of state. Parameterizing the dark fluid equation of state by a variety of polynomials in the scale factor $a$, we use current kinematical data to constrain the parameters. While the simplest interpretation of the dark fluid remains that it is comprised of separate dark matter and cosmological constant contributions, our results cover other model types including unified dark energy/matter scenarios.

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Optimizing baryon acoustic oscillation surveys II: curvature, redshifts, and external datasets

We extend our study of the optimization of large baryon acoustic oscillation (BAO) surveys to return the best constraints on the dark energy, building on Paper I of this series (Parkinson et al. 2007). The survey galaxies are assumed to be pre-selected active, star-forming galaxies observed by their line emission with a constant number density across the redshift bin. Star-forming galaxies have a redshift desert in the region 1.6 < z < 2, and so this redshift range was excluded from the analysis. We use the Seo & Eisenstein (2007) fitting formula for the accuracies of the BAO measurements, using only the information for the oscillatory part of the power spectrum as distance and expansion rate rulers. We go beyond our earlier analysis by examining the effect of including curvature on the optimal survey configuration and updating the expected `prior' constraints from Planck and SDSS. We once again find that the optimal survey strategy involves minimizing the exposure time and maximizing the survey area (within the instrumental constraints), and that all time should be spent observing in the low-redshift range (z<1.6) rather than beyond the redshift desert, z>2. We find that when assuming a flat universe the optimal survey makes measurements in the redshift range 0.1 < z <0.7, but that including curvature as a nuisance parameter requires us to push the maximum redshift to 1.35, to remove the degeneracy between curvature and evolving dark energy. The inclusion of expected other data sets (such as WiggleZ, BOSS and a stage III SN-Ia survey) removes the necessity of measurements below redshift 0.9, and pushes the maximum redshift up to 1.5. We discuss considerations in determining the best survey strategy in light of uncertainty in the true underlying cosmological model.

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A New Approach to Multi-wavelength Associations of Astronomical Sources

One of the biggest problems faced by current and next-generation astronomical surveys is trying to produce large numbers of accurate cross identifications across a range of wavelength regimes with varying data quality and positional uncertainty. Until recently simple spatial "nearest neighbour" associations have been sufficient for most applications. However as advances in instrumentation allow more sensitive images to be made the rapid increase in the source density has meant that source confusion across multiple wavelengths is a serious problem. The field of far-IR and sub-mm astronomy has been particularly hampered by such problems. The poor angular resolution of current sub-mm and far-IR instruments is such that in a lot of cases there are multiple plausible counterparts for each source at other wavelengths. Here we present a new automated method of producing associations between sources at different wavelengths using a combination of spatial and SED information set in the Bayesian framework presented by Budavari & Szalay (2008). Testing of the technique is performed on both simulated catalogues of sources from GaLICS and real data from multi-wavelength observations of the SXDF. It is found that a single figure of merit, the Bayes factor, can be effectively used to describe the confidence in the match. Further applications of this technique to future Herschel datasets are discussed.

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Planck priors for dark energy surveys

Although cosmic microwave background (CMB) anisotropy data alone cannot constrain simultaneously the spatial curvature and the equation of state of dark energy, CMB data provide a valuable addition to other experimental results. However computing a full CMB power spectrum with a Boltzmann code is quite slow; for instance if we want to work with many dark energy and/or modified gravity models, or would like to optimize experiments where many different configurations need to be tested, it is possible to adopt a quicker and more efficient approach. In this paper we consider the compression of the projected Planck CMB data into four parameters, R (scaled distance to last scattering surface), l_a (angular scale of sound horizon at last scattering), Omega_b h^2 (baryon density fraction) and n_s (powerlaw index of primordial matter power spectrum), all of which can be computed quickly. We show that, although this compression loses information compared to the full likelihood, such information loss becomes negligible when more data is added. We also demonstrate that the method can be used for scalar field dark energy independently of the parametrisation of the equation of state, and discuss how this method should be used for other kinds of dark energy models.

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When can the Planck satellite measure spectral index running?

We use model selection forecasting to assess the ability of the Planck satellite to make a positive detection of spectral index running. We simulate Planck data for a range of assumed cosmological parameter values, and carry out a three-way Bayesian model comparison of a Harrison-Zel'dovich model, a power-law model, and a model including running. We find that Planck will be able to strongly support running only if its true value satisfies |dn/d ln k| > 0.02.

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Optimizing future imaging survey of galaxies to confront dark energy and modified gravity models

We consider the extent to which future imaging surveys of galaxies can distinguish between dark energy and modified gravity models for the origin of the cosmic acceleration. Dynamical dark energy models may have similar expansion rates as models of modified gravity, yet predict different growth of structure histories. We parameterize the cosmic expansion by the two parameters, $w_0$ and $w_a$, and the linear growth rate of density fluctuations by Linder's $γ$, independently. Dark energy models generically predict $γ\approx 0.55$, while the DGP model $γ\approx 0.68$. To determine if future imaging surveys can constrain $γ$ within 20 percent (or $Δγ<0.1$), we perform the Fisher matrix analysis for a weak lensing survey such as the on-going Hyper Suprime-Cam (HSC) project. Under the condition that the total observation time is fixed, we compute the Figure of Merit (FoM) as a function of the exposure time $\texp$. We find that the tomography technique effectively improves the FoM, which has a broad peak around $\texp\simeq {\rm several}\sim 10$ minutes; a shallow and wide survey is preferred to constrain the $γ$ parameter. While $Δγ< 0.1$ cannot be achieved by the HSC weak-lensing survey alone, one can improve the constraints by combining with a follow-up spectroscopic survey like WFMOS and/or future CMB observations.

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Optimising Baryon Acoustic Oscillation Surveys - I: Testing the concordance LCDM cosmology

We optimize the design of future spectroscopic redshift surveys for constraining the dark energy via precision measurements of the baryon acoustic oscillations (BAO), with particular emphasis on the design of the Wide-Field Multi-Object Spectrograph (WFMOS). We develop a model that predicts the number density of possible target galaxies as a function of exposure time and redshift. We use this number counts model together with fitting formulae for the accuracy of the BAO measurements to determine the effectiveness of different surveys and instrument designs. We search through the available survey parameter space to find the optimal survey with respect to the dark energy equation-of-state parameters according to the Dark Energy Task Force Figure-of-Merit, including predictions of future measurements from the Planck satellite. We optimize the survey to test the LambdaCDM model, assuming that galaxies are pre-selected using photometric redshifts to have a constant number density with redshift, and using a non-linear cut-off for the matter power spectrum that evolves with redshift. We find that line-emission galaxies are strongly preferred as targets over continuum emission galaxies. The optimal survey covers a redshift range 0.8 < z < 1.4, over the widest possible area (6000 sq. degs from 1500 hours observing time). The most efficient number of fibres for the spectrograph is 2,000, and the survey performance continues to improve with the addition of extra fibres until a plateau is reached at 10,000 fibres. The optimal point in the survey parameter space is not highly peaked and is not significantly affected by including constraints from upcoming supernovae surveys and other BAO experiments.

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Comment on `Tainted evidence: cosmological model selection versus fitting', by Eric V. Linder and Ramon Miquel (astro-ph/0702542v2)

In astro-ph/0702542v2, Linder and Miquel seek to criticize the use of Bayesian model selection for data analysis and for survey forecasting and design. Their discussion is based on three serious misunderstandings of the conceptual underpinnings and application of model-level Bayesian inference, which invalidate all their main conclusions. Their paper includes numerous further inaccuracies, including an erroneous calculation of the Bayesian Information Criterion. Here we seek to set the record straight.

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