Coincidence and coherent data analysis methods for gravitational wave bursts in a network of interferometric detectors

Network data analysis methods are the only way to properly separate real gravitational wave (GW) transient events from detector noise. They can be divided into two generic classes: the coincidence method and the coherent analysis. The former uses lists of selected events provided by each interferometer belonging to the network and tries to correlate them in time to identify a physical signal. Instead of this binary treatment of detector outputs (signal present or absent), the latter method involves first the merging of the interferometer data and looks for a common pattern, consistent with an assumed GW waveform and a given source location in the sky. The thresholds are only applied later, to validate or not the hypothesis made. As coherent algorithms use a more complete information than coincidence methods, they are expected to provide better detection performances, but at a higher computational cost. An efficient filter must yield a good compromise between a low false alarm rate (hence triggering on data at a manageable rate) and a high detection efficiency. Therefore, the comparison of the two approaches is achieved using so-called Receiving Operating Characteristics (ROC), giving the relationship between the false alarm rate and the detection efficiency for a given method. This paper investigates this question via Monte-Carlo simulations, using the network model developed in a previous article.Comment: Spelling mistake corrected in one author's nam

A Time Domain Waveform for Testing General Relativity

Gravitational-wave parameter estimation is only as good as the theory the waveform generation models are based upon. It is therefore crucial to test General Relativity (GR) once data becomes available. Many previous works, such as studies connected with the ppE framework by Yunes and Pretorius, rely on the stationary phase approximation (SPA) to model deviations from GR in the frequency domain. As Fast Fourier Transform algorithms have become considerably faster and in order to circumvent possible problems with the SPA, we test GR with corrected time domain waveforms instead of SPA waveforms. Since a considerable amount of work has been done already in the field using SPA waveforms, we establish a connection between leading-order-corrected waveforms in time and frequency domain, concentrating on phase-only corrected terms. In a Markov Chain Monte Carlo study, whose results are preliminary and will only be available later, we will assess the ability of the eLISA detector to measure deviations from GR for signals coming from supermassive black hole inspirals using these corrected waveforms.Comment: 5 pages. Proceedings of LISA Symposium X, submitted to Journal of Physics: Conference Serie

Particle physics methodologies applied to time-of-flight positron emission tomography with silicon-photomultipliers and inorganic scintillators

Positron emission tomography, or PET, is a medical imaging technique which has been used in clinical environments for over two decades. With the advent of fast timing detectors and scintillating crystals, it is possible to envisage improvements to the technique with the inclusion of time-of-flight capabilities. In this context, silicon photomultipliers coupled to fast inorganic LYSO crystals are investigated as a possible technology choice. As part of the ENVISION collaboration a range of photon detectors were investigated experimentally, leading to the selection of specific devices for use in a first prototype detector, currently being commissioned at the Rutherford Appleton Laboratory. In order to characterise the design of the prototype a GEANT4 simulation has been developed describing coupled systems of silicon photomultipliers and LYSO scintillators. Very good agreement is seen between the timing response of the experimental and simulated systems. Results of the simulation for a range of detector array arrangements are presented and a number of optimisations proposed for the final prototype design. Without the results provided here a detector system including only 3x3x5 mm3 crystals would have been adopted. A 3x3x5 mm3 crystal geometry is shown to provide little-to-no timing advantage over an identical system with 3x3x10 mm3 crystals, where detection efficiency is improved by approximately a factor of three. Additionally an investigation is presented which explores the impact of using events where gamma-ray photons are scattered internally within the detector array. It is shown that including such events could increase the signal achievable with one-to-one coupled detector arrays systems for PET by approximately 60%, with only minor reductions in coincidence timing resolution

Automatic CNN channel selection and effective detection on face and rotated aerial objects

Balancing accuracy and computational cost is a challenging task in computer vision. This is especially true for convolutional neural networks (CNNs), which required far larger scale of processing power than traditional learning algorithms. This thesis is aimed at the development of new CNN structures and loss functions to tackle the unbalanced accuracy-effciency issue in image classification and object detection, which are two fundamental yet challenging tasks of computer vision. For a CNN based object detector, the main computational cost is caused by the feature extractor (backbone), which has been originally applied to image classification.;Optimising the structure of CNN applied to image classification will bring benefits when it is applied to object detection. Although the outputs of detectors may vary across detection tasks, the challenges and the design principles among detectors are similar. Therefore, this thesis will start with face detection (i.e. a single object detection task), which is a significant branch of objection detection and has been widely used in real life. After that, object detection on aerial image will be investigated, which is a more challenging detection task.;Specifically, the objectives of this thesis are: 1. Optimising the CNN structures for image classification; 2. Developing a face detector which enables a trade-off between computational cost and accuracy; and 3. Proposing an object detector for aerial images, which suppresses the background noise without damaging the inference efficiency.;For the first target, this thesis aims to automatically optimise the topology of CNNs to generate the structure of fixed-length models, in which unnecessary convolutional kernels are removed. Experimental results have demonstrated that the optimised model can achieve comparable accuracy to the state-of-the-art models, across a broad range of datasets, whilst significantly reducing the number of parameters.;To tackle the unbalanced accuracy-effciency challenge in face detection, a novel context enhanced approach is proposed which improves the performance of the face detector in terms of both loss function and structure. For loss function optimisation, a hierarchical loss, referred to as 'triple loss' in this thesis, is introduced to optimise the feature pyramid network (FPN) based face detector. For structural optimisation, this thesis proposes a context-sensitive structure to increase the capacity of the network prediction. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost of face detection.;To suppress the background noise in aerial image object detection, this thesis presents a two-stage detector, named as 'SAFDet'. To be more specific, a rotation anchor-free-branch (RAFB) is proposed to regress the precise rectangle boundary. Asthe RAFB is anchor free, the computational cost is negligible during training. Meanwhile,a centre prediction module (CPM) is introduced to enhance the capabilities oftarget localisation and noise suppression from the background. As the CPM is only deployed during training, it does not increase the computational cost of inference. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost, and it effectively suppresses the background noise at the same time.Balancing accuracy and computational cost is a challenging task in computer vision. This is especially true for convolutional neural networks (CNNs), which required far larger scale of processing power than traditional learning algorithms. This thesis is aimed at the development of new CNN structures and loss functions to tackle the unbalanced accuracy-effciency issue in image classification and object detection, which are two fundamental yet challenging tasks of computer vision. For a CNN based object detector, the main computational cost is caused by the feature extractor (backbone), which has been originally applied to image classification.;Optimising the structure of CNN applied to image classification will bring benefits when it is applied to object detection. Although the outputs of detectors may vary across detection tasks, the challenges and the design principles among detectors are similar. Therefore, this thesis will start with face detection (i.e. a single object detection task), which is a significant branch of objection detection and has been widely used in real life. After that, object detection on aerial image will be investigated, which is a more challenging detection task.;Specifically, the objectives of this thesis are: 1. Optimising the CNN structures for image classification; 2. Developing a face detector which enables a trade-off between computational cost and accuracy; and 3. Proposing an object detector for aerial images, which suppresses the background noise without damaging the inference efficiency.;For the first target, this thesis aims to automatically optimise the topology of CNNs to generate the structure of fixed-length models, in which unnecessary convolutional kernels are removed. Experimental results have demonstrated that the optimised model can achieve comparable accuracy to the state-of-the-art models, across a broad range of datasets, whilst significantly reducing the number of parameters.;To tackle the unbalanced accuracy-effciency challenge in face detection, a novel context enhanced approach is proposed which improves the performance of the face detector in terms of both loss function and structure. For loss function optimisation, a hierarchical loss, referred to as 'triple loss' in this thesis, is introduced to optimise the feature pyramid network (FPN) based face detector. For structural optimisation, this thesis proposes a context-sensitive structure to increase the capacity of the network prediction. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost of face detection.;To suppress the background noise in aerial image object detection, this thesis presents a two-stage detector, named as 'SAFDet'. To be more specific, a rotation anchor-free-branch (RAFB) is proposed to regress the precise rectangle boundary. Asthe RAFB is anchor free, the computational cost is negligible during training. Meanwhile,a centre prediction module (CPM) is introduced to enhance the capabilities oftarget localisation and noise suppression from the background. As the CPM is only deployed during training, it does not increase the computational cost of inference. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost, and it effectively suppresses the background noise at the same time

Sparse Shape Modelling for 3D Face Analysis

This thesis describes a new method for localising anthropometric landmark points on 3D face scans. The points are localised by fitting a sparse shape model to a set of candidate landmarks. The candidates are found using a feature detector that is designed using a data driven methodology, this approach also informs the choice of landmarks for the shape model. The fitting procedure is developed to be robust to missing landmark data and spurious candidates. The feature detector and landmark choice is determined by the performance of different local surface descriptions on the face. A number of criteria are defined for a good landmark point and good feature detector. These inform a framework for measuring the performance of various surface descriptions and the choice of parameter values in the surface description generation. Two types of surface description are tested: curvature and spin images. These descriptions, in many ways, represent many aspects of the two most common approaches to local surface description. Using the data driven design process for surface description and landmark choice, a feature detector is developed using spin images. As spin images are a rich surface description, we are able to perform detection and candidate landmark labelling in a single step. A feature detector is developed based on linear discriminant analysis (LDA). This is compared to a simpler detector used in the landmark and surface description selection process. A sparse shape model is constructed using ground truth landmark data. This sparse shape model contains only the landmark point locations and relative positional variation. To localise landmarks, this model is fitted to the candidate landmarks using a RANSAC style algorithm and a novel model fitting algorithm. The results of landmark localisation show that the shape model approach is beneficial over template alignment approaches. Even with heavily contaminated candidate data, we are able to achieve good localisation for most landmarks