18731427
Joint Minkowski functionals and bispectrum constraints on non-Gaussianity in the cosmic microwave background
Two of the most commonly used tools to constrain the primordial non-Gaussianity are the bispectrum and the Minkowski functionals of cosmic microwave background temperature anisotropies. These two measures of non-Gaussianity in principle provide distinct (though correlated) information, but in the past constraints from them have only been loosely compared and not statistically combined. In this work we evaluate, for the first time, the covariance matrix between the local non-Gaussianity coefficient ${f}_{\mathrm{NL}}$ estimated through the bispectrum and Minkowski functionals. We find that the estimators are positively correlated, with correlation coefficient $r\ensuremath{\simeq}0.3$. Using the WMAP7 data to combine the two measures and accounting for the point-source systematics, we find the combined constraint ${f}_{\mathrm{NL}}=37\ifmmode\pm\else\textpm\fi{}28$, which has a $\ensuremath{\sim}20%$ smaller error than either of the individual constraints.
54769355
Scale-dependent non-Gaussianity as a generalization of the local model
We generalize the local model of primordial non-Gaussianity by promoting the parameter fNL to a general scale-dependent function fNL(k). We calculate the resulting bispectrum and the eect on the bias of dark matter halos, and thus the extent to which fNL(k) can be measured from the large-scale structure observations. By calculating the principal components of fNL(k), we identify scales where this form of non-Gaussianity is best constrained and estimate the overlap with previously studied local and equilateral non- Gaussian models.
10108151
First constraints on the running of non-Gaussianity.
We use data from the Wilkinson Microwave Anisotropy probe temperature maps to constrain a scale-dependent generalization of the popular "local" model for primordial non-Gaussianity. In the model where the parameter f(NL) is allowed to run with scale k, f(NL)(k) = f*(NL) (k/k(piv))(n)(fNL), we constrain the running to be n(f)(NL) = 0.30(-1.2)(+1.9) at 95% confidence, marginalized over the amplitude f*(NL). The constraints depend somewhat on the prior probabilities assigned to the two parameters. In the near future, constraints from a combination of Planck and large-scale structure surveys are expected to improve this limit by about an order of magnitude and usefully constrain classes of inflationary models.
118815799
Photometric Confirmation of MACHO Large Magellanic Cloud Microlensing Events
We present previously unpublished photometry of three Large Magellanic Cloud (LMC) microlensing events and show that the new photometry confirms the microlensing interpretation of these events. These events were discovered by the MACHO Project alert system and were also recovered by the analysis of the 5.7 yr MACHO data set. This new photometry provides a substantial increase in the signal-to-noise ratio over the previously published photometry, and in all three cases, the gravitational microlensing interpretation of these events is strengthened. The new data consist of MACHO-Global Microlensing Alert Network (GMAN) follow-up images from the CTIO 0.9 m telescope plus difference imaging photometry of the original MACHO data from the 1.3 m Great Melbourne telescope at Mount Stromlo. We also combine microlensing light-curve fitting with photometry from high-resolution HST images of the source stars to provide further confirmation of these events and to show that the microlensing interpretation of event MACHO LMC-23 is questionable. Finally, we compare our results with the analysis of Belokurov et al., who have attempted to classify candidate microlensing events with a neural network method, and we find that their results are contradicted by the new data and more powerful light-curve fitting analysis for each of the four events considered in this paper. The failure of the Belokurov et al. method is likely to be due to their use of a set of insensitive statistics to feed their neural networks.
118610276
Regularization techniques for PSF-matching kernels – I. Choice of kernel basis
We review current methods for building point spread function (PSF)-matching kernels for the purposes of image subtraction or co-addition. Such methods use a linear decomposition of the kernel on a series of basis functions. The correct choice of these basis functions is fundamental to the efficiency and effectiveness of the matching – the chosen bases should represent the underlying signal using a reasonably small number of shapes, and/or have a minimum number of user-adjustable tuning parameters. We examine methods whose bases comprise multiple Gauss–Hermite polynomials, as well as a form-free basis composed of delta-functions. Kernels derived from delta-functions are unsurprisingly shown to be more expressive; they are able to take more general shapes and perform better in situations where sum-of-Gaussian methods are known to fail. However, due to its many degrees of freedom (the maximum number allowed by the kernel size) this basis tends to overfit the problem and yields noisy kernels having large variance. We introduce a new technique to regularize these delta-function kernel solutions, which bridges the gap between the generality of delta-function kernels and the compactness of sum-of-Gaussian kernels. Through this regularization we are able to create general kernel solutions that represent the intrinsic shape of the PSF-matching kernel with only one degree of freedom, the strength of the regularization λ. The role of λ is effectively to exchange variance in the resulting difference image with variance in the kernel itself. We examine considerations in choosing the value of λ, including statistical risk estimators and the ability of the solution to predict solutions for adjacent areas. Both of these suggest moderate strengths of λ between 0.1 and 1.0, although this optimization is likely data set dependent. This model allows for flexible representations of the convolution kernel that have significant predictive ability and will prove useful in implementing robust image subtraction pipelines that must address hundreds to thousands of images per night.
119431407
The MACHO Project LMC Variable Star Inventory: Classical Cepheids and AGB Variables
This paper has been withdrawn by the author.
41669275
A DESCRIPTION OF QUASAR VARIABILITY MEASURED USING REPEATED SDSS AND POSS IMAGING
We provide a quantitative description and statistical interpretation of the optical continuum variability of quasars. The Sloan Digital Sky Survey (SDSS) has obtained repeated imaging in five UV-to-IR photometric bands for 33,881 spectroscopically confirmed quasars. About 10,000 quasars have an average of 60 observations in each band obtained over a decade along Stripe 82 (S82), whereas the remaining ~25,000 have 2-3 observations due to scan overlaps. The observed time lags span the range from a day to almost 10 years, and constrain quasar variability at rest-frame time lags of up to 4 years, and at rest-frame wavelengths from 1000 A to 6000 A. We publicly release a user-friendly catalog of quasars from the SDSS Data Release 7 that have been observed at least twice in SDSS or once in both SDSS and the Palomar Observatory Sky Survey, and we use it to analyze the ensemble properties of quasar variability. Based on a damped random walk (DRW) model defined by a characteristic timescale and an asymptotic variability amplitude that scale with the luminosity, black hole mass, and rest wavelength for individual quasars calibrated in S82, we can fully explain the ensemble variability statistics of the non-S82 quasars such as the exponential distribution of large magnitude changes. All available data are consistent with the DRW model as a viable description of the optical continuum variability of quasars on timescales of ~5-2000 days in the rest frame. We use these models to predict the incidence of quasar contamination in transient surveys such as those from the Palomar Transient Factory and Large Synoptic Survey Telescope.