Crossing Statistic: Bayesian interpretation, model selection and resolving dark energy parametrization problem

By introducing Crossing functions and hyper-parameters I show that the Bayesian interpretation of the Crossing Statistics [1] can be used trivially for the purpose of model selection among cosmological models. In this approach to falsify a cosmological model there is no need to compare it with other models or assume any particular form of parametrization for the cosmological quantities like luminosity distance, Hubble parameter or equation of state of dark energy. Instead, hyper-parameters of Crossing functions perform as discriminators between correct and wrong models. Using this approach one can falsify any assumed cosmological model without putting priors on the underlying actual model of the universe and its parameters, hence the issue of dark energy parametrization is resolved. It will be also shown that the sensitivity of the method to the intrinsic dispersion of the data is small that is another important characteristic of the method in testing cosmological models dealing with data with high uncertainties.Comment: 14 pages, 4 figures, discussions extended, 1 figure and two references added, main results unchanged, matches the final version to be published in JCA

Observational constraint on dynamical evolution of dark energy

We use the Constitution supernova, the baryon acoustic oscillation, the cosmic microwave background, and the Hubble parameter data to analyze the evolution property of dark energy. We obtain different results when we fit different baryon acoustic oscillation data combined with the Constitution supernova data to the Chevallier-Polarski-Linder model. We find that the difference stems from the different values of $\Omega_{m0}$. We also fit the observational data to the model independent piecewise constant parametrization. Four redshift bins with boundaries at $z=0.22$, 0.53, 0.85 and 1.8 were chosen for the piecewise constant parametrization of the equation of state parameter $w(z)$ of dark energy. We find no significant evidence for evolving $w(z)$. With the addition of the Hubble parameter, the constraint on the equation of state parameter at high redshift isimproved by 70%. The marginalization of the nuisance parameter connected to the supernova distance modulus is discussed.Comment: revtex, 16 pages, 5 figures, V2: published versio

Interacting models may be key to solve the cosmic coincidence problem

It is argued that cosmological models that feature a flow of energy from dark energy to dark matter may solve the coincidence problem of late acceleration (i.e., "why the energy densities of both components are of the same order precisely today?"). However, much refined and abundant observational data of the redshift evolution of the Hubble factor are needed to ascertain whether they can do the job.Comment: 25 pages, 11 figures; accepted for publication in JCA

The Crossing Statistic: Dealing with Unknown Errors in the Dispersion of Type Ia Supernovae

We propose a new statistic that has been designed to be used in situations where the intrinsic dispersion of a data set is not well known: The Crossing Statistic. This statistic is in general less sensitive than `chi^2' to the intrinsic dispersion of the data, and hence allows us to make progress in distinguishing between different models using goodness of fit to the data even when the errors involved are poorly understood. The proposed statistic makes use of the shape and trends of a model's predictions in a quantifiable manner. It is applicable to a variety of circumstances, although we consider it to be especially well suited to the task of distinguishing between different cosmological models using type Ia supernovae. We show that this statistic can easily distinguish between different models in cases where the `chi^2' statistic fails. We also show that the last mode of the Crossing Statistic is identical to `chi^2', so that it can be considered as a generalization of `chi^2'.Comment: 14 pages, 5 figures. Paper restructured and extended and new interpretation of the method presented. New results concerning model selection. Treatment and error-analysis made fully model independent. References added. Accepted for publication in JCA

A Process for the Creation of T-MATS Propulsion System Models from NPSS Data

A modular thermodynamic simulation package called the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) has been developed for the creation of dynamic simulations. The T-MATS software is designed as a plug-in for Simulink(Registered TradeMark) and allows a developer to create system simulations of thermodynamic plants (such as gas turbines) and controllers in a single tool. Creation of such simulations can be accomplished by matching data from actual systems, or by matching data from steady state models and inserting appropriate dynamics, such as the rotor and actuator dynamics for an aircraft engine. This paper summarizes the process for creating T-MATS turbo-machinery simulations using data and input files obtained from a steady state model created in the Numerical Propulsion System Simulation (NPSS). The NPSS is a thermodynamic simulation environment that is commonly used for steady state gas turbine performance analysis. Completion of all the steps involved in the process results in a good match between T-MATS and NPSS at several steady state operating points. Additionally, the T-MATS model extended to run dynamically provides the possibility of simulating and evaluating closed loop responses

Moisture susceptibility of high and low compaction dry process crumb rubber modified asphalt mixtures

The field performance of dry process crumb rubber-modified (CRM) asphalt mixtures has been reported to be inconsistent with stripping and premature cracking on the surfacing. One of the concerns is that, because achieving field compaction of CRM material is difficult due to the inherent resilient nature of the rubber particle, nonuniform field compaction may lead to a deficient bond between rubber and bitumen. To assess the influence of compaction, a series of CRM and control mixtures was produced and compacted at two levels: 4% (low, optimum laboratory compaction) and 8% (high, field experience) air void content. The long-term durability, in regard to moisture susceptibility of the mixtures, was assessed by conducting repeated moisture conditioning cycles. Mechanical properties (stiffness, fatigue, and resistance to permanent deformation) were determined in the Nottingham Asphalt Tester. Results indicated that compared with conventional mixtures, the CRM mixtures, regardless of compaction effort, are more susceptible to moisture with the degree of susceptibility primarily depending on the amount of rubber in the mixture, rather than the difference in compaction. This behavior is different from that of conventional mixtures in which, as expected, poorly compacted mixtures were found to be more susceptible to moisture than were well-compacted mixtures

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Comparison of Recent SnIa datasets

We rank the six latest Type Ia supernova (SnIa) datasets (Constitution (C), Union (U), ESSENCE (Davis) (E), Gold06 (G), SNLS 1yr (S) and SDSS-II (D)) in the context of the Chevalier-Polarski-Linder (CPL) parametrization $w(a)=w_0+w_1 (1-a)$, according to their Figure of Merit (FoM), their consistency with the cosmological constant ($\Lambda$CDM), their consistency with standard rulers (Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations (BAO)) and their mutual consistency. We find a significant improvement of the FoM (defined as the inverse area of the 95.4% parameter contour) with the number of SnIa of these datasets ((C) highest FoM, (U), (G), (D), (E), (S) lowest FoM). Standard rulers (CMB+BAO) have a better FoM by about a factor of 3, compared to the highest FoM SnIa dataset (C). We also find that the ranking sequence based on consistency with $\Lambda$CDM is identical with the corresponding ranking based on consistency with standard rulers ((S) most consistent, (D), (C), (E), (U), (G) least consistent). The ranking sequence of the datasets however changes when we consider the consistency with an expansion history corresponding to evolving dark energy $(w_0,w_1)=(-1.4,2)$ crossing the phantom divide line $w=-1$ (it is practically reversed to (G), (U), (E), (S), (D), (C)). The SALT2 and MLCS2k2 fitters are also compared and some peculiar features of the SDSS-II dataset when standardized with the MLCS2k2 fitter are pointed out. Finally, we construct a statistic to estimate the internal consistency of a collection of SnIa datasets. We find that even though there is good consistency among most samples taken from the above datasets, this consistency decreases significantly when the Gold06 (G) dataset is included in the sample.Comment: 13 pages, 9 figures. Included recently released SDSS-II dataset. Improved presentation. Main results unchanged. The mathematica files and datasets used for the production of the figures may be downloaded from http://leandros.physics.uoi.gr/datacomp

Coupled oscillators as models of phantom and scalar field cosmologies

We study a toy model for phantom cosmology recently introduced in the literature and consisting of two oscillators, one of which carries negative kinetic energy. The results are compared with the exact phase space picture obtained for similar dynamical systems describing, respectively, a massive canonical scalar field conformally coupled to the spacetime curvature, and a conformally coupled massive phantom. Finally, the dynamical system describing exactly a minimally coupled phantom is studied and compared with the toy model.Comment: 18 pages, LaTeX, to appear in Physical Review