Abstract
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 framework for cosmic string inference from observations of the CMB made over the celestial sphere, performing 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 Gμ 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 Gμ ∼ 5 × 10−7 for Nambu–Goto string simulations that include an integrated Sachs–Wolfe contribution only and do not include any recombination effects, before any parameters of the analysis are optimized. The sensitivity of the method compares favourably with other techniques applied to the same simulations.
We explore the impact of an update to the typical approximation for the shape noise term in the analytic covariance matrix for cosmic shear experiments that assumes the absence of survey boundary and ...mask effects. We present an exact expression for the number of galaxy pairs in this term based on the survey mask, which leads to more than a factor of three increase in the shape noise on the largest measured scales for the Kilo-Degree Survey (KiDS-450) real-space cosmic shear data. We compare the result of this analytic expression to several alternative methods for measuring the shape noise from the data and find excellent agreement. This update to the covariance resolves any internal model tension evidenced by the previously large cosmological best-fitting χ2 for the KiDS-450 cosmic shear data. The best-fitting χ2 is reduced from 161 to 121 for 118 degrees of freedom. We also apply a correction to how the multiplicative shear calibration uncertainty is included in the covariance. This change shifts the inferred amplitude of the correlation function to higher values. In conclusion, we find that this improves agreement of the KiDS-450 cosmic shear results with Dark Energy Survey Year 1 and Planck results.
We measure the cross-correlation between the galaxy density in the Dark Energy Survey (DES) Science Verification data and the lensing of the cosmic microwave background (CMB) as reconstructed with ...the Planck satellite and the South Pole Telescope (SPT). When using the DES main galaxy sample over the full redshift range 0.2 < z
phot < 1.2, a cross-correlation signal is detected at 6σ and 4σ with SPT and Planck , respectively. We then divide the DES galaxies into five photometric redshift bins, finding significant (>2σ) detections in all bins. Comparing to the fiducial Planck cosmology, we find the redshift evolution of the signal matches expectations, although the amplitude is consistently lower than predicted across redshift bins. We test for possible systematics that could affect our result and find no evidence for significant contamination. Finally, we demonstrate how these measurements can be used to constrain the growth of structure across cosmic time. We find the data are fit by a model in which the amplitude of structure in the z < 1.2 universe is 0.73 ± 0.16 times as large as predicted in the Λ cold dark matter Planck cosmology, a 1.7σ deviation.
ABSTRACT
With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a ...dramatic increase in data volume. Machine learning techniques are well suited to address this data challenge and rapidly classify newly detected transients. We present a multiwavelength classification algorithm consisting of three steps: (1) interpolation and augmentation of the data using Gaussian processes; (2) feature extraction using wavelets; and (3) classification with random forests. Augmentation provides improved performance at test time by balancing the classes and adding diversity into the training set. In the first application of machine learning to the classification of real radio transient data, we apply our technique to the Green Bank Interferometer and other radio light curves. We find we are able to accurately classify most of the 11 classes of radio variables and transients after just eight hours of observations, achieving an overall test accuracy of 78 per cent. We fully investigate the impact of the small sample size of 82 publicly available light curves and use data augmentation techniques to mitigate the effect. We also show that on a significantly larger simulated representative training set that the algorithm achieves an overall accuracy of 97 per cent, illustrating that the method is likely to provide excellent performance on future surveys. Finally, we demonstrate the effectiveness of simultaneous multiwavelength observations by showing how incorporating just one optical data point into the analysis improves the accuracy of the worst performing class by 19 per cent.
A simple cosmological model with only six parameters (matter density, Omega sub(m)h super(2), baryon density, Omega sub(b)h super(2), Hubble constant, H sub(0), amplitude of fluctuations, sigma ...sub(8), optical depth, tau , and a slope for the scalar perturbation spectrum, n sub(s)) fits not only the 3 year WMAP temperature and polarization data, but also small-scale CMB data, light element abundances, large-scale structure observations, and the supernova luminosity/distance relationship. Using WMAP data, only, the best-fit values for cosmological parameters for the power-law flat Lambda cold dark matter ( Lambda CDM) model are ( Omega sub(m)h super(2), Omega sub(b)h super(2),h,n sub(s), tau , sigma sub(8)) = (0.1277 super(+0.0080)-0.0079,0.02229 plus or minus 0.00073,0.732 super(+0.031)-0.032,0.958 plus or minus 0.016,0.089 plus or minus 0.030,0.761 super(+0.049)-0.048). The 3 year data dramatically shrink the allowed volume in mis six-dimensional parameter space. Assuming that the primordial fluctuations are adiabatic with a power-law spectrum, the WMAP data alone require dark matter and favor a spectral index that is significantly less than the Harrison-Zel'dovich-Peebles scale-invariant spectrum (n sub(s) = 1, r = 0). Adding additional data sets improves the constraints on these components and the spectral slope. For power-law models, WMAP data alone puts an improved upper limit on the tensor-to-scalar ratio, r sub(0.002) < 0.65 (95% CL) and the combination of WMAP and the lensing-normalized SDSS galaxy survey implies r sub(0.002) < 0.30 (95% CL). Models that suppress large-scale power through a running spectral index or a large-scale cutoff in the power spectrum are a better fit to the WMAP and small-scale CMB data than the power-law Lambda CDM model; however, the improvement in the fit to the WMAP data is only Delta chi super(2) = 3 for 1 extra degree of freedom. Models with a running-spectral index are consistent with a higher amplitude of gravity waves. In a flat universe, the combination of WMAP and the Supernova Legacy Survey (SNLS) data yields a significant constraint on the equation of state of the dark energy, w = -0.967 super(+0.073)-0.072. If we assume w = -1, then the deviations from the critical density, Omega sub(K) are small: the combination of WMAP and the SNLS data implies Omega sub(k) = -0.011 plus or minus 0.012. The combination of WMAP 3 year data plus the HST Key Project constraint on H sub(0) implies Omega sub(k) = -0.014 plus or minus 0.017 and Omega sub( Lambda ) = 0.716 plus or minus 0.055. Even if we do not include the prior that the universe is flat, by combining WMAP, large-scale structure, and supernova data, we can still put a strong constraint on the dark energy equation of state, w = -1.08 plus or minus 0.12. For a flat universe, the combination of WMAP and other astronomical data yield a constraint on the sum of the neutrino masses, capital sigma m sub( upsilon ) < 0.66 eV (95%CL). Consistent with the predictions of simple inflationary theories, we detect no significant deviations from Gaussianity in the CMB maps using Minkowski functionals, the bispectrum, trispectrum, and a new statistic designed to detect large-scale anisotropies in the fluctuations.
We present a kinematic study of the nuclear stellar disk in M31 at infrared wavelengths using high spatial resolution integral field spectroscopy. The spatial resolution achieved, FWHM = 0 12 (0.45 ...pc at the distance of M31), has only previously been equaled in spectroscopic studies by space-based long-slit observations. Using adaptive-optics-corrected integral field spectroscopy from the OSIRIS instrument at the W. M. Keck Observatory, we map the line-of-sight kinematics over the entire old stellar eccentric disk orbiting the supermassive black hole (SMBH) at a distance of r < 4 pc. The peak velocity dispersion is 381 55 km s−1, offset by 0 13 0 03 from the SMBH, consistent with previous high-resolution long-slit observations. There is a lack of near-infrared (NIR) emission at the position of the SMBH and young nuclear cluster, suggesting a spatial separation between the young and old stellar populations within the nucleus. We compare the observed kinematics with dynamical models from Peiris & Tremaine. The best-fit disk orientation to the NIR flux is θl, θi, θa = −33° 4°, 44° 2°, −15° 15°, which is tilted with respect to both the larger-scale galactic disk and the best-fit orientation derived from optical observations. The precession rate of the old disk is P = 0.0 3.9 km s−1 pc−1, lower than the majority of previous observations. This slow precession rate suggests that stellar winds from the disk will collide and shock, driving rapid gas inflows and fueling an episodic central starburst as suggested in Chang et al.
We introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric ...large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z ∈ 0.2, 0.8. Our fiducial sample has a comoving space density of 10−3 (h
−1 Mpc)−3, and a median photo-z bias (z
spec − z
photo) and scatter (σ
z
/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.
We present measurements of weak gravitational lensing cosmic shear two-point statistics using Dark Energy Survey Science Verification data. We demonstrate that our results are robust to the choice of ...shear measurement pipeline, either ngmix or im3shape, and robust to the choice of two-point statistic, including both real and Fourier-space statistics. Our results pass a suite of null tests including tests for B-mode contamination and direct tests for any dependence of the two-point functions on a set of 16 observing conditions and galaxy properties, such as seeing, airmass, galaxy color, galaxy magnitude, etc. We furthermore use a large suite of simulations to compute the covariance matrix of the cosmic shear measurements and assign statistical significance to our null tests. We find that our covariance matrix is consistent with the halo model prediction, indicating that it has the appropriate level of halo sample variance. We compare the same jackknife procedure applied to the data and the simulations in order to search for additional sources of noise not captured by the simulations. We find no statistically significant extra sources of noise in the data. The overall detection significance with tomography for our highest source density catalog is 9.7sigma. Cosmological constraints from the measurements in this work are presented in a companion paper DES et al., Phys. Rev. D 94, 022001 (2016)..
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