The sensitivity of searches for astrophysical transients in data from the LIGO is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detecto
rs is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These glitches can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGOs fourth science run and one week of LIGOs sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract is already being used.
There is a broad class of astrophysical sources that produce detectable, transient, gravitational waves. Some searches for transient gravitational waves are tailored to known features of these sources. Other searches make few assumptions about the so
urces. Typically events are observable with multiple search techniques. This work describes how to combine the results of searches that are not independent, treating each search as a classifier for a given event. This will be shown to improve the overall sensitivity to gravitational-wave events while directly addressing the problem of consistent interpretation of multiple trials.
We describe a general approach to detection of transient gravitational-wave signals in the presence of non-Gaussian background noise. We prove that under quite general conditions, the ratio of the likelihood of observed data to contain a signal to th
e likelihood of it being a noise fluctuation provides optimal ranking for the candidate events found in an experiment. The likelihood-ratio ranking allows us to combine different kinds of data into a single analysis. We apply the general framework to the problem of unifying the results of independent experiments and the problem of accounting for non-Gaussian artifacts in the searches for gravitational waves from compact binary coalescence in LIGO data. We show analytically and confirm through simulations that in both cases the likelihood ratio statistic results in an improved analysis.
A signature of the dark energy equation of state may be observed in the shape of voids. We estimate the constraints on cosmological parameters that would be determined from the ellipticity distribution of voids from future spectroscopic surveys alrea
dy planned for the study of large scale structure. The constraints stem from the sensitivity of the distribution of ellipticity to the cosmological parameters through the variance of fluctuations of the density field smoothed at some length scale. This length scale can be chosen to be of the order of the comoving radii of voids at very early times when the fluctuations are Gaussian distributed. We use Fisher estimates to show that the constraints from void ellipticities are promising. Combining these constraints with other traditional methods results in the improvement of the Dark Energy Task Force Figure of Merit on the dark energy parameters by an order of hundred for future experiments. The estimates of these future constraints depend on a number of systematic issues which require further study using simulations. We outline these issues and study the impact of certain observational and theoretical systematics on the forecasted constraints on dark energy parameters.
Are geometrical summaries of the CMB and LSS sufficient for estimating cosmological parameters? And how does our choice of a dark energy model impact the current constraints on standard cosmological parameters? We address these questions in the con
text of the widely used CPL parametrization of a time varying equation of state w in a cosmology allowing spatial curvature. We study examples of different behavior allowed in a CPL parametrization in a phase diagram, and relate these to effects on the observables. We examine parameter constraints in such a cosmology by combining WMAP5, SDSS, SNe, HST data sets by comparing the power spectra. We carefully quantify the differences of these constraints to those obtained by using geometrical summaries for the same data sets. We find that (a) using summary parameters instead of the full data sets give parameter constraints that are similar, but with discernible differences, (b) due to degeneracies, the constraints on the standard parameters broaden significantly for the same data sets. In particular, we find that in the context of CPL dark energy, (i) a Harrison-Zeldovich spectrum cannot be ruled out at $2sigma$ levels with our current data sets. and (ii) the SNe IA, HST, and WMAP 5 data are not sufficient to constrain spatial curvature; we additionally require the SDSS DR4 data to achieve this.
The use of the loudest observed event to generate statistical statements about rate and strength has become standard in searches for gravitational waves from compact binaries and pulsars. The Bayesian formulation of the method is generalized in this
paper to allow for uncertainties both in the background estimate and in the properties of the population being constrained. The method is also extended to allow rate interval construction. Finally, it is shown how to combine the results from multiple experiments and a comparison is drawn between the upper limit obtained in a single search and the upper limit obtained by combining the results of two experiments each of half the original duration. To illustrate this, we look at an example case, motivated by the search for gravitational waves from binary inspiral.
