Impact of temperature dependence of the energy loss on jet quenching observables

The quenching of jets (particles with $p_T>>T, \Lambda_{QCD}$) in ultra-relativistic heavy-ion collisions has been one of the main prediction and discovery at RHIC. We have studied, by a simple jet quenching modeling, the correlation between different observables like the nuclear modification factor \Rapt, the elliptic flow $v_2$ and the ratio of quark to gluon suppression $R_{AA}(quark)/R_{AA}(gluon)$. We show that the relation among these observables is strongly affected by the temperature dependence of the energy loss. In particular the large $v_2$ and and the nearly equal \Rapt of quarks and gluons can be accounted for only if the energy loss occurs mainly around the temperature $T_c$ and the flavour conversion is significant.Finally we point out that the efficency in the conversion of the space eccentricity into the momentum one ($v_2$) results to be quite smaller respect to the one coming from elastic scatterings in a fluid with a viscosity to entropy density ratio $4\pi\eta/s=1$.Comment: 7 pages, 8 figures, Workshop WISH 201

Asymptotic robustness of Kelly's GLRT and Adaptive Matched Filter detector under model misspecification

A fundamental assumption underling any Hypothesis Testing (HT) problem is that the available data follow the parametric model assumed to derive the test statistic. Nevertheless, a perfect match between the true and the assumed data models cannot be achieved in many practical applications. In all these cases, it is advisable to use a robust decision test, i.e. a test whose statistic preserves (at least asymptotically) the same probability density function (pdf) for a suitable set of possible input data models under the null hypothesis. Building upon the seminal work of Kent (1982), in this paper we investigate the impact of the model mismatch in a recurring HT problem in radar signal processing applications: testing the mean of a set of Complex Elliptically Symmetric (CES) distributed random vectors under a possible misspecified, Gaussian data model. In particular, by using this general misspecified framework, a new look to two popular detectors, the Kelly's Generalized Likelihood Ration Test (GLRT) and the Adaptive Matched Filter (AMF), is provided and their robustness properties investigated.Comment: ISI World Statistics Congress 2017 (ISI2017), Marrakech, Morocco, 16-21 July 201

Elliptic Flow from Non-equilibrium Initial Condition with a Saturation Scale

A current goal of relativistic heavy ion collisions experiments is the search for a Color Glass Condensate as the limiting state of QCD matter at very high density. In viscous hydrodynamics simulations, a standard Glauber initial condition leads to estimate $4\pi \eta/s \sim 1$, while a Color Glass Condensate modeling leads to at least a factor of 2 larger $\eta/s$. Within a kinetic theory approach based on a relativistic Boltzmann-like transport simulation, we point out that the out-of-equilibrium initial distribution proper of a Color Glass Condensate reduces the efficiency in building-up the elliptic flow. Our main result at RHIC energy is that the available data on $v_2$ are in agreement with a $4\pi \eta/s \sim 1$ also for Color Glass Condensate initial conditions, opening the possibility to describe self-consistently also higher order flow, otherwise significantly underestimated, and to pursue further the search for signatures of the Color Glass Condensate.Comment: 6 pages, 4 figures. // Title changed, some discussion added, main conclusions unchanged. Version accepted for publication on Phys. Lett.

Introduction to the Special Issue on Liminal Hotspots

This article introduces a special issue of Theory and Psychology on liminal hotspots. A liminal hotspot is an occasion during which people feel they are caught suspended in the circumstances of a transition that has become permanent. The liminal experiences of ambiguity and uncertainty that are typically at play in transitional circumstances acquire an enduring quality that can be described as a “hotspot”. Liminal hotspots are characterized by dynamics of paradox, paralysis, and polarization, but they also intensify the potential for pattern shift. The origins of the concept are described followed by an overview of the contributions to this special issue

Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental findings and applications

Inferring information from a set of acquired data is the main objective of any signal processing (SP) method. In particular, the common problem of estimating the value of a vector of parameters from a set of noisy measurements is at the core of a plethora of scientific and technological advances in the last decades; for example, wireless communications, radar and sonar, biomedicine, image processing, and seismology, just to name a few. Developing an estimation algorithm often begins by assuming a statistical model for the measured data, i.e. a probability density function (pdf) which if correct, fully characterizes the behaviour of the collected data/measurements. Experience with real data, however, often exposes the limitations of any assumed data model since modelling errors at some level are always present. Consequently, the true data model and the model assumed to derive the estimation algorithm could differ. When this happens, the model is said to be mismatched or misspecified. Therefore, understanding the possible performance loss or regret that an estimation algorithm could experience under model misspecification is of crucial importance for any SP practitioner. Further, understanding the limits on the performance of any estimator subject to model misspecification is of practical interest. Motivated by the widespread and practical need to assess the performance of a mismatched estimator, the goal of this paper is to help to bring attention to the main theoretical findings on estimation theory, and in particular on lower bounds under model misspecification, that have been published in the statistical and econometrical literature in the last fifty years. Secondly, some applications are discussed to illustrate the broad range of areas and problems to which this framework extends, and consequently the numerous opportunities available for SP researchers.Comment: To appear in the IEEE Signal Processing Magazin

High-order maximum-entropy collocation methods

This paper considers the approximation of partial differential equations with a point collocation framework based on high-order local maximum-entropy schemes (HOLMES). In this approach, smooth basis functions are computed through an optimization procedure and the strong form of the problem is directly imposed at the collocation points, reducing significantly the computational times with respect to the Galerkin formulation. Furthermore, such a method is truly meshless, since no background integration grids are necessary. The validity of the proposed methodology is verified with supportive numerical examples, where the expected convergence rates are obtained. This includes the approximation of PDEs on domains bounded by implicit and explicit (NURBS) curves, illustrating a direct integration between the geometric modeling and the numerical analysis

Instabilities in a Mean-field dynamics of Asymmetric Nuclear Matter

We discuss the features of instabilities in asymmetric nuclear matter, in particular the relation between the nature of fluctuations, the types of instabilities and the properties of the interaction. We show a chemical instability appears as an instability against isoscalar-like fluctuations. Then starting from phenomenological hadronic field theory (QHD), including exchange terms, we discuss the symmetry energy and the relation to the dynamical response inside the spinodal region.Comment: 8 pages, 5 Postscript figures, talk at Cortona 2000 Conference, Oct. 17 - Oct. 20, Italy, World Scientific (in press