The Kinematics of Multiple-Peaked Lyα Emission in Star-Forming Galaxies at z ~ 2-3

We present new results on the Lyα emission-line kinematics of 18 z ~ 2-3 star-forming galaxies with multiple-peaked Lyα profiles. With our large spectroscopic database of UV-selected star-forming galaxies at these redshifts, we have determined that ~30% of such objects with detectable Lyα emission display multiple-peaked emission profiles. These profiles provide additional constraints on the escape of Lyα photons due to the rich velocity structure in the emergent line. Despite recent advances in modeling the escape of Lyα from star-forming galaxies at high redshifts, comparisons between models and data are often missing crucial observational information. Using Keck II NIRSPEC spectra of Hα (z ~ 2) and [O III]λ5007 (z ~ 3), we have measured accurate systemic redshifts, rest-frame optical nebular velocity dispersions, and emission-line fluxes for the objects in the sample. In addition, rest-frame UV luminosities and colors provide estimates of star formation rates and the degree of dust extinction. In concert with the profile sub-structure, these measurements provide critical constraints on the geometry and kinematics of interstellar gas in high-redshift galaxies. Accurate systemic redshifts allow us to translate the multiple-peaked Lyα profiles into velocity space, revealing that the majority (11/18) display double-peaked emission straddling the velocity-field zero point with stronger red-side emission. Interstellar absorption-line kinematics suggest the presence of large-scale outflows for the majority of objects in our sample, with an average measured interstellar absorption velocity offset of (Δv_(abs))=–230 km s^(–1). A comparison of the interstellar absorption kinematics for objects with multiple- and single-peaked Lyα profiles indicate that the multiple-peaked objects are characterized by significantly narrower absorption line widths. We compare our data with the predictions of simple models for outflowing and infalling gas distributions around high-redshift galaxies. While popular "shell" models provide a qualitative match with many of the observations of Lyα emission, we find that in detail there are important discrepancies between the models and data, as well as problems with applying the framework of an expanding thin shell of gas to explain high-redshift galaxy spectra. Our data highlight these inconsistencies, as well as illuminating critical elements for success in future models of outflow and infall in high-redshift galaxies

A review of the neural basis underlying the acoustic startle response with a focus on recent developments in mammals

The startle response consists of whole-body muscle contractions, eye-blink, accelerated heart rate, and freezing in response to a strong, sudden stimulus. It is evolutionarily preserved and can be observed in any animal that can perceive sensory signals, indicating the important protective function of startle. Startle response measurements and its alterations have become a valuable tool for exploring sensorimotor processes and sensory gating, especially in the context of pathologies of psychiatric disorders. The last reviews on the neural substrates underlying acoustic startle were published around 20 years ago. Advancements in methods and techniques have since allowed new insights into acoustic startle mechanisms. This review is focused on the neural circuitry that drives the primary acoustic startle response in mammals. However, there have also been very successful efforts to identify the acoustic startle pathway in other vertebrates and invertebrates in the past decades, so at the end we briefly summarize these studies and comment on the similarities and differences between species

A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

A Survey of Statistical Network Models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active “network community” and a substantial liter- ature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning net- work literature in statistical physics and computer science. The growthof the World Wide Web and the emergence of online “networking com- munities” such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize for- mal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Statistic

Full-Polarization Observations of OH Masers in Massive Star-Forming Regions: I. Data

We present full-polarization VLBA maps of the ground-state, main-line, 2 Pi 3/2, J = 3/2 OH masers in 18 Galactic massive star-forming regions. This is the first large polarization survey of interstellar hydroxyl masers at VLBI resolution. A total of 184 Zeeman pairs are identified, and the corresponding magnetic field strengths are indicated. We also present spectra of the NH3 emission or absorption in these star-forming regions. Analysis of these data will be presented in a companion paper.Comment: 111 pages, including 42 figures and 21 tables, to appear in ApJ