Soft Contribution to the Hard Ridge in Relativistic Nuclear Collisions

Nuclear collisions exhibit long-range rapidity correlations not present in proton-proton collisions. Because the correlation structure is wide in relative pseudorapidity and narrow in relative azimuthal angle, it is known as the ridge. Similar ridge structures are observed in correlations of particles associated with a jet trigger (the hard ridge) as well as correlations without a trigger (the soft ridge). Earlier we argued that the soft ridge arises when particles formed in an early Glasma stage later manifest transverse flow. We extend this study to address new soft ridge measurements. We then determine the contribution of flow to the hard ridge.Comment: 16 pages, 9 figures, includes comparison to dat

The flow of the Antarctic circumpolar current over the North Scotia Ridge

The transports associated with the Subantarctic Front (SAF) and the Polar Front (PF) account for the majority of the volume transport of the Antarctic Circumpolar Current (ACC). After passing through Drake Passage, the SAF and the PF veer northward over the steep topography of the North Scotia Ridge. Interaction of the ACC with the North Scotia Ridge influences the sources of the Malvinas Current. This ridge is a major obstacle to the flow of deep water, with the majority of the deep water passing through the 3100 m deep gap in the ridge known as Shag Rocks Passage. Volume transports associated with these fronts were measured during the North Scotia Ridge Overflow Project, which included the first extensive hydrographic survey of the ridge, carried out in April and May 2003. The total net volume transport northward over the ridge was found to be . The total net transport associated with the SAF was approximately , and the total transport associated with the PF was approximately . Weddell Sea Deep Water was not detected passing through Shag Rocks Passage, contrary to some previous inferences

Is the ridge formed by aligned jet propagation and medium flow?

Motivated by the recent observation that the ridge decreases with trigger particle angle ($\phi_s$) relative to the event plane, it is theorized that the ridge is formed by interplay between jet propagation and medium flow. Such interplay may produce asymmetry in the ridge azimuthal correlation at a fixed $\phi_s$. We present an analysis of this asymmetry from STAR data. We found an asymmetric ridge with maximum asymmetry at $\phi_s\approx45^{\circ}$ concurrent with a symmetric jet at all $\phi_s$.Comment: 2 pages, 2 figures, Quark Matter 200

U.S. Extended Continental Shelf Cruise to Map Gaps in Kela and Karin Ridges, Johnston Atoll, Equatorial Pacific Ocean

The objectives for cruise KM14-17 are to map the bathymetry of two gaps in two submarine ridges in the vicinity of Johnston Atoll. One ridge gap occurs along the informally named Keli Ridge (Hein et al., 2005) south of Johnston Atoll and the other ridge gap occurs north of Johnston Atoll that separates Sculpin Ridge (also informally called Karin Ridge) and Horizon Ridge, all in the central equatorial Pacific (Fig. 1). The cruise took advantage of a scheduled dead-head transit from Papeete, Tahiti to Honolulu, Hawai’i that could be extended for 5 days to include the planned mapping. The mapping is in support of the U.S. (Extended Continental Shelf (ECS) Task Force. These areas were identified by the ECS Central Pacific Integrated Regional Team as having the potential for an ECS

High resolution bathymetric survey on the NW slope of Walvis Ridge, offshore Namibia

Expedition 17/1 of the German research vessel R/V MARIA S. MERIAN, carried out geophysical surveys and experiments between November and December 2010 in the area around Walvis Ridge, Southeast Atlantic Ocean. Among the data collected, a high-resolution bathymetric dataset aquired on the northwestern slope of the ridge offers some important preliminary insights into the tectonic evolution of the ridge and the adjoining lower continental slopes and ocean basin. The NE-SW trending Walvis Ridge has a trapezoid shape and is likely built up by thick sequences of plateau basalts, with top of basement rocks inclined to the south. Sediments are almost absent on the NW side of the ridge, preserving a fascinating mountainscape formed early in the tectonic history, most probably on-land. This interpretation is supported by clear denudational features, like steep cliffs up to 150 m high, and deeply incised valleys, defining paleo-drainages. Isolated, flat-topped guyots seaward of the ocean-continent boundary attest to a later history of wave abrasion and progressive subsidence of Walvis Ridge

The Matrix Ridge Approximation: Algorithms and Applications

We are concerned with an approximation problem for a symmetric positive semidefinite matrix due to motivation from a class of nonlinear machine learning methods. We discuss an approximation approach that we call {matrix ridge approximation}. In particular, we define the matrix ridge approximation as an incomplete matrix factorization plus a ridge term. Moreover, we present probabilistic interpretations using a normal latent variable model and a Wishart model for this approximation approach. The idea behind the latent variable model in turn leads us to an efficient EM iterative method for handling the matrix ridge approximation problem. Finally, we illustrate the applications of the approximation approach in multivariate data analysis. Empirical studies in spectral clustering and Gaussian process regression show that the matrix ridge approximation with the EM iteration is potentially useful

Lecture notes on ridge regression

The linear regression model cannot be fitted to high-dimensional data, as the high-dimensionality brings about empirical non-identifiability. Penalized regression overcomes this non-identifiability by augmentation of the loss function by a penalty (i.e. a function of regression coefficients). The ridge penalty is the sum of squared regression coefficients, giving rise to ridge regression. Here many aspect of ridge regression are reviewed e.g. moments, mean squared error, its equivalence to constrained estimation, and its relation to Bayesian regression. Finally, its behaviour and use are illustrated in simulation and on omics data. Subsequently, ridge regression is generalized to allow for a more general penalty. The ridge penalization framework is then translated to logistic regression and its properties are shown to carry over. To contrast ridge penalized estimation, the final chapter introduces its lasso counterpart

Energy and system dependence of high-pTp_T triggered two-particle near-side correlations

Previous studies have indicated that the near-side peak of high-$p_T$ triggered correlations can be decomposed into two parts, the \textit{Jet} and the \textit{Ridge}. We present data on the yield per trigger of the \textit{Jet} and the \textit{Ridge} from $d+Au$, $Cu+Cu$ and $Au+Au$ collisions at $\sqrt{s_{NN}}$ = 62.4 GeV and 200 GeV and compare data on the \textit{Jet} to PYTHIA 8.1 simulations for $p+p$. PYTHIA describes the \textit{Jet} component up to a scaling factor, meaning that PYTHIA can provide a better understanding of the \textit{Ridge} by giving insight into the effects of the kinematic cuts. We present collision energy and system dependence of the \textit{Ridge} yield, which should help distinguish models for the production mechanism of the \textit{Ridge}.Comment: 4 pages, 6 figures, proceedings for Hot Quarks in Estes Park, Colorad