Non-Gaussian Foreground Residuals of the WMAP First Year Maps

We investigate the effect of foreground residuals in the WMAP data (Bennet et al. 2004) by adding foreground contamination to Gaussian ensembles of CMB signal and noise maps. We evaluate a set of non-Gaussian estimators on the contaminated ensembles to determine with what accuracy any residual in the data can be constrained using higher order statistics. We apply the estimators to the raw and cleaned Q, V, and W band first year maps. The foreground subtraction method applied to clean the data in Bennet et al. (2004a) appears to have induced a correlation between the power spectra and normalized bispectra of the maps which is absent in Gaussian simulations. It also appears to increase the correlation between the dl=1 inter-l bispectrum of the cleaned maps and the foreground templates. In a number of cases the significance of the effect is above the 98% confidence level.Comment: 9 pages, 4 figure

Primeval symmetries

A detailed examination of the Killing equations in Robertson-Walker coordinates shows how the addition of matter and/or radiation to a de Sitter Universe breaks the symmetry generated by four of its Killing fields. The product U = (a^2)(dH/dt) of the squared scale parameter by the time-derivative of the Hubble function encapsulates the relationship between the two cases: the symmetry is maximal when U is a constant, and reduces to the six-parameter symmetry of a generic Friedmann-Robertson-Walker model when it is not. As the fields physical interpretation is not clear in these coordinates, comparison is made with the Killing fields in static coordinates, whose interpretation is made clearer by their direct relationship to the Poincare group generators via Wigner-Inonu contractions.Comment: 16 pages, 2 tables; published versio

Log Skeletons: A Classification Approach to Process Discovery

To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify whether or not a new trace conforms to the event log, the better the discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art fully-automated discovery algorithms score poorly on this classification. Even the best of these algorithms, the Inductive Miner, scored only 147 correct classified traces out of 200 traces on the PDC of 2017. This paper introduces the rule-based log skeleton model, which is closely related to the Declare constraint model, together with a way to classify traces using this model. This classification using log skeletons is shown to score better on the PDC of 2017 than state-of-the-art discovery algorithms: 194 out of 200. As a result, one can argue that the fully-automated algorithm to construct (or: discover) a log skeleton from an event log outperforms existing state-of-the-art fully-automated discovery algorithms.Comment: 16 pages with 9 figures, followed by an appendix of 14 pages with 17 figure

Nonextensivity in the solar magnetic activity during the increasing phase of solar Cycle 23

In this paper we analyze the behavior of the daily Sunspot Number from the Sunspot Index Data Center (SIDC), the mean Magnetic Field strength from the National Solar Observatory/Kitt Peak (NSO/KP) and Total Solar Irradiance means from Virgo/SoHO, in the context of the $q$--Triplet which emerges within nonextensive statistical mechanics. Distributions for the mean solar Magnetic Field show two different behaviors, with a $q$--Gaussian for scales of 1 to 16 days and a Gaussian for scales longer than 32 days. The latter corresponds to an equilibrium state. Distributions for Total Solar Irradiance also show two different behaviors (approximately Gaussian) for scales of 128 days and longer, consistent with statistical equilibrium and $q$--Gaussian for scales $<$ 128 days. Distributions for the Sunspot Number show a $q$--Gaussian independent of timescales, consistent with a nonequilibrium state. The values obtained ("$q$--Triplet"$\equiv$$\{$$q$$_{stat}$,$q$$_{sen}$,$q$$_{rel}$$\}$) demonstrate that the Gaussian or $q$--Gaussian behavior of the aforementioned data depends significantly on timescales. These results point to strong multifractal behavior of the dataset analyzed, with the multifractal level decreasing from Sunspot Number to Total Solar Irradiance. In addition, we found a numerically satisfied dual relation between $q_{stat}$ and $q_{sen}$.Comment: 6 pages, 4 figure

Non-Chern-Simons Topological Mass Generation in (2+1) Dimensions

By dimensional reduction of a massive BF theory, a new topological field theory is constructed in (2+1) dimensions. Two different topological terms, one involving a scalar and a Kalb-Ramond fields and another one equivalent to the four-dimensional BF term, are present. We constructed two actions with these topological terms and show that a topological mass generation mechanism can be implemented. Using the non-Chern-Simons topological term, an action is proposed leading to a classical duality relation between Klein-Gordon and Maxwell actions. We also have shown that an action in (2+1) dimensions with the Kalb-Ramond field is related by Buscher's duality transformation to a massive gauge-invariant Stuckelberg-type theory.Comment: 8 pages, no figures, RevTE

Principal Component Analysis as a Tool for Characterizing Black Hole Images and Variability

We explore the use of principal component analysis (PCA) to characterize high-fidelity simulations and interferometric observations of the millimeter emission that originates near the horizons of accreting black holes. We show mathematically that the Fourier transforms of eigenimages derived from PCA applied to an ensemble of images in the spatial-domain are identical to the eigenvectors of PCA applied to the ensemble of the Fourier transforms of the images, which suggests that this approach may be applied to modeling the sparse interferometric Fourier-visibilities produced by an array such as the Event Horizon Telescope (EHT). We also show that the simulations in the spatial domain themselves can be compactly represented with a PCA-derived basis of eigenimages allowing for detailed comparisons between variable observations and time-dependent models, as well as for detection of outliers or rare events within a time series of images. Furthermore, we demonstrate that the spectrum of PCA eigenvalues is a diagnostic of the power spectrum of the structure and, hence, of the underlying physical processes in the simulated and observed images.Comment: 16 pages, 17 figures, submitted to Ap