Study of the acoustic signature of UHE neutrino interactions in water and ice

The production of acoustic signals from the interactions of ultra-high energy (UHE) cosmic ray neutrinos in water and ice has been studied. A new computationally fast and efficient method of deriving the signal is presented. This method allows the implementation of up to date parameterisations of acoustic attenuation in sea water and ice that now includes the effects of complex attenuation, where appropriate. The methods presented here have been used to compute and study the properties of the acoustic signals which would be expected from such interactions. A matrix method of parameterising the signals, which includes the expected fluctuations, is also presented. These methods are used to generate the expected signals that would be detected in acoustic UHE neutrino telescopes.Comment: 21 pages and 13 figure

Belle II Technical Design Report

The Belle detector at the KEKB electron-positron collider has collected almost 1 billion Y(4S) events in its decade of operation. Super-KEKB, an upgrade of KEKB is under construction, to increase the luminosity by two orders of magnitude during a three-year shutdown, with an ultimate goal of 8E35 /cm^2 /s luminosity. To exploit the increased luminosity, an upgrade of the Belle detector has been proposed. A new international collaboration Belle-II, is being formed. The Technical Design Report presents physics motivation, basic methods of the accelerator upgrade, as well as key improvements of the detector.Comment: Edited by: Z. Dole\v{z}al and S. Un

Focusing Cosmic Telescopes: Exploring Redshift z~5-6 Galaxies with the Bullet Cluster 1E0657-56

The gravitational potential of clusters of galaxies acts as a cosmic telescope allowing us to find and study galaxies at fainter limits than otherwise possible and thus probe closer to the epoch of formation of the first galaxies. We use the Bullet Cluster 1E0657-56 (z = 0.296) as a case study, because its high mass and merging configuration makes it one of the most efficient cosmic telescopes we know. We develop a new algorithm to reconstruct the gravitational potential of the Bullet Cluster, based on a non-uniform adaptive grid, combining strong and weak gravitational lensing data derived from deep HST/ACS F606W-F775W-F850LP and ground-based imaging. We exploit this improved mass map to study z~5-6 Lyman Break Galaxies (LBGs), which we detect as dropouts. One of the LBGs is multiply imaged, providing a geometric confirmation of its high redshift, and is used to further improve our mass model. We quantify the uncertainties in the magnification map reconstruction in the intrinsic source luminosity, and in the volume surveyed, and show that they are negligible compared to sample variance when determining the luminosity function of high-redshift galaxies. With shallower and comparable magnitude limits to HUDF and GOODS, the Bullet cluster observations, after correcting for magnification, probe deeper into the luminosity function of the high redshift galaxies than GOODS and only slightly shallower than HUDF. We conclude that accurately focused cosmic telescopes are the most efficient way to sample the bright end of the luminosity function of high redshift galaxies and - in case they are multiply imaged - confirm their redshifts.Comment: 12 pages, Accepted for publication in Ap

Analysis framework for the prompt discovery of compact binary mergers in gravitational-wave data

We describe a stream-based analysis pipeline to detect gravitational waves from the merger of binary neutron stars, binary black holes, and neutron-star–black-hole binaries within ∼1 min of the arrival of the merger signal at Earth. Such low-latency detection is crucial for the prompt response by electromagnetic facilities in order to observe any fading electromagnetic counterparts that might be produced by mergers involving at least one neutron star. Even for systems expected not to produce counterparts, low-latency analysis of the data is useful for deciding when not to point telescopes, and as feedback to observatory operations. Analysts using this pipeline were the first to identify GW151226, the second gravitational-wave event ever detected. The pipeline also operates in an offline mode, in which it incorporates more refined information about data quality and employs acausal methods that are inapplicable to the online mode. The pipeline’s offline mode was used in the detection of the first two gravitational-wave events, GW150914 and GW151226, as well as the identification of a third candidate, LVT151012

Autonomous navigation for guide following in crowded indoor environments

The requirements for assisted living are rapidly changing as the number of elderly patients over the age of 60 continues to increase. This rise places a high level of stress on nurse practitioners who must care for more patients than they are capable. As this trend is expected to continue, new technology will be required to help care for patients. Mobile robots present an opportunity to help alleviate the stress on nurse practitioners by monitoring and performing remedial tasks for elderly patients. In order to produce mobile robots with the ability to perform these tasks, however, many challenges must be overcome. The hospital environment requires a high level of safety to prevent patient injury. Any facility that uses mobile robots, therefore, must be able to ensure that no harm will come to patients whilst in a care environment. This requires the robot to build a high level of understanding about the environment and the people with close proximity to the robot. Hitherto, most mobile robots have used vision-based sensors or 2D laser range finders. 3D time-of-flight sensors have recently been introduced and provide dense 3D point clouds of the environment at real-time frame rates. This provides mobile robots with previously unavailable dense information in real-time. I investigate the use of time-of-flight cameras for mobile robot navigation in crowded environments in this thesis. A unified framework to allow the robot to follow a guide through an indoor environment safely and efficiently is presented. Each component of the framework is analyzed in detail, with real-world scenarios illustrating its practical use. Time-of-flight cameras are relatively new sensors and, therefore, have inherent problems that must be overcome to receive consistent and accurate data. I propose a novel and practical probabilistic framework to overcome many of the inherent problems in this thesis. The framework fuses multiple depth maps with color information forming a reliable and consistent view of the world. In order for the robot to interact with the environment, contextual information is required. To this end, I propose a region-growing segmentation algorithm to group points based on surface characteristics, surface normal and surface curvature. The segmentation process creates a distinct set of surfaces, however, only a limited amount of contextual information is available to allow for interaction. Therefore, a novel classifier is proposed using spherical harmonics to differentiate people from all other objects. The added ability to identify people allows the robot to find potential candidates to follow. However, for safe navigation, the robot must continuously track all visible objects to obtain positional and velocity information. A multi-object tracking system is investigated to track visible objects reliably using multiple cues, shape and color. The tracking system allows the robot to react to the dynamic nature of people by building an estimate of the motion flow. This flow provides the robot with the necessary information to determine where and at what speeds it is safe to drive. In addition, a novel search strategy is proposed to allow the robot to recover a guide who has left the field-of-view. To achieve this, a search map is constructed with areas of the environment ranked according to how likely they are to reveal the guide’s true location. Then, the robot can approach the most likely search area to recover the guide. Finally, all components presented are joined to follow a guide through an indoor environment. The results achieved demonstrate the efficacy of the proposed components

Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm