Extragalactic Radio Continuum Surveys and the Transformation of Radio Astronomy

Next-generation radio surveys are about to transform radio astronomy by discovering and studying tens of millions of previously unknown radio sources. These surveys will provide new insights to understand the evolution of galaxies, measuring the evolution of the cosmic star formation rate, and rivalling traditional techniques in the measurement of fundamental cosmological parameters. By observing a new volume of observational parameter space, they are also likely to discover unexpected new phenomena. This review traces the evolution of extragalactic radio continuum surveys from the earliest days of radio astronomy to the present, and identifies the challenges that must be overcome to achieve this transformational change.Comment: To be published in Nature Astronomy 18 Sept 201

CERES: Clouds and the Earth's Radiant Energy System

This brochure gives a brief description of the science research that is being done with data from the Clouds and Earth's Radiant Energy System (CERES) instrument flying onboard NASA's Terra satellite. It also contains information about some of the data products and technical specifications. Educational levels: Undergraduate lower division, Undergraduate upper division, Graduate or professional

Global Optimization for Future Gravitational Wave Detectors' Sites

We consider the optimal site selection of future generations of gravitational wave detectors. Previously, Raffai et al. optimized a 2-detector network with a combined figure of merit. This optimization was extended to networks with more than two detectors in a limited way by first fixing the parameters of all other component detectors. In this work we now present a more general optimization that allows the locations of all detectors to be simultaneously chosen. We follow the definition of Raffai et al. on the metric that defines the suitability of a certain detector network. Given the locations of the component detectors in the network, we compute a measure of the network's ability to distinguish the polarization, constrain the sky localization and reconstruct the parameters of a gravitational wave source. We further define the `flexibility index' for a possible site location, by counting the number of multi-detector networks with a sufficiently high Figure of Merit that include that site location. We confirm the conclusion of Raffai et al., that in terms of flexibility index as defined in this work, Australia hosts the best candidate site to build a future generation gravitational wave detector. This conclusion is valid for either a 3-detector network or a 5-detector network. For a 3-detector network site locations in Northern Europe display a comparable flexibility index to sites in Australia. However for a 5-detector network, Australia is found to be a clearly better candidate than any other location.Comment: 30 pages, 23 figures, 2 table

Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning

Desenvolvimento de um sistema capaz de realizar a deteção e classificação de obstáculos de vários tipos que possam ser sujeitos de colisões e resultar em danos para a embarcação ou até na destruição total do mesmo. O sistema é também capaz da deteção da linha do horizonte para estimar a distância relativa dos objetos detetados à posição atual da embarcação. As deteções são conseguidas recorrendo a técnicas de Deep Learning, nomeadamente usando CNNs, para a deteção dos obstaculos e linha do horizonte.Development of a system capable of obstacle detection and classification of various types that may be subject of collisions and result in damages to the ship or even its own total loss. The system is also capable of detection the horizon line, to estimate the relative distance of the detected objects to the vehicle current position. This is achieved throught Deep Learning techniques, namely by the use of Convolutional Neural Networks

The ANTARES Collaboration: Contributions to ICRC 2017 Part II: The multi-messenger program

Papers on the ANTARES multi-messenger program, prepared for the 35th International Cosmic Ray Conference (ICRC 2017, Busan, South Korea) by the ANTARES Collaboratio

A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy

The analysis of data from gravitational wave detectors can be divided into three phases: search, characterization, and evaluation. The evaluation of the detection - determining whether a candidate event is astrophysical in origin or some artifact created by instrument noise - is a crucial step in the analysis. The on-going analyses of data from ground based detectors employ a frequentist approach to the detection problem. A detection statistic is chosen, for which background levels and detection efficiencies are estimated from Monte Carlo studies. This approach frames the detection problem in terms of an infinite collection of trials, with the actual measurement corresponding to some realization of this hypothetical set. Here we explore an alternative, Bayesian approach to the detection problem, that considers prior information and the actual data in hand. Our particular focus is on the computational techniques used to implement the Bayesian analysis. We find that the Parallel Tempered Markov Chain Monte Carlo (PTMCMC) algorithm is able to address all three phases of the anaylsis in a coherent framework. The signals are found by locating the posterior modes, the model parameters are characterized by mapping out the joint posterior distribution, and finally, the model evidence is computed by thermodynamic integration. As a demonstration, we consider the detection problem of selecting between models describing the data as instrument noise, or instrument noise plus the signal from a single compact galactic binary. The evidence ratios, or Bayes factors, computed by the PTMCMC algorithm are found to be in close agreement with those computed using a Reversible Jump Markov Chain Monte Carlo algorithm.Comment: 19 pages, 12 figures, revised to address referee's comment