Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter-estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ

Binary Neutron Star Mergers: Gravitational-wave Measurements of Their Parameters and the Nuclear Equation of State

Since making the first direct detection of gravitational waves in 2015, the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) together with the Virgo observatory has detected an additional 51 confirmed signals from binary mergers. Two of these signals, GW170817 and GW190425, were identified as binary neutron star mergers. As detector sensitivity improves we expect to see many more binary neutron star merger events, both from future observing runs of the LIGO-Virgo network and from planned third-generation detectors. These new detections will provide an exquisite look at the nature of these systems and of neutron stars themselves. This thesis describes how gravitational-wave observations of neutron star mergers can be used to measure the properties of the binary systems and the fundamental physics of neutron stars. We use multimessenger observations of GW170817 to measure its viewing angle, which is important to understand the engine driving the electromagnetic counterpart to the gravitational-wave signal. We describe a new implementation of a fast likelihood model for gravitational-wave parameter estimation. We demonstrate that this likelihood allows analysis of binary neutron star signals to be performed quickly enough to inform strategies for electromagnetic follow-up observations. We measure the tidal deformabilities and radii of the neutron stars in GW170817, imposing a physical constraint to require that both neutron stars obey a common nuclear equation of state. We assess the future prospects for measuring the nuclear equation of state with the LIGO-Virgo network and with the planned third-generation detector, Cosmic Explorer

Software packages performance evaluation of basic radar signal processing techniques

This dissertation presents a radar signal processing infrastructure implemented on scripting language platforms. The main goal is to determine if any open source scripted packages are appropriate for radar signal processing and if it is worthwhile purchasing the more expensive MATLAB, commonly used in industry. Some of the most common radar signal processing techniques were considered, such as pulse compression, Doppler processing and adaptive filtering for interference suppression. The scripting languages investigated were the proprietary MATLAB, as well as open source alternatives such as Octave, Scilab, Python and Julia. While the experiments were conducted, it was decided that the implementations should have algorithmic fairness across the various software packages. The first experiment was loop based pulse compression and Doppler processing algorithms, where Julia and Python outperformed the rest. A further analysis was completed by using vectors to index matrices instead of loops, where possible. This saw a significant improvement in all of the languages for Doppler processing implementations. Although Julia performed extremely well in terms of speed, it utilized the most memory for the processing techniques. This was due to its garbage collector not automatically clearing the memory heap when required. The adaptive LMS (least mean squares) filter designs were a different form of analysis, as a vector of data was required instead of a matrix of data. When processing a vector or one dimensional array of data, Julia outperformed the rest of the software packages significantly, approximately a 10 times speed improvement. The experiments indicated that Python performed satisfactorily in terms of speed and memory utilization. Physical RAM of computer systems is, however, constantly improving, which will mitigate the memory issue for Julia. Overall, Julia is the best open source software package to use, as its syntax is similar to MATLAB compared with Python, and it is improving rapidly as Julia developers are constantly updating it. Other disadvantage of Python is that the mathematical signal processing is an add-on realized by modules such as NumPy

Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

The Advanced LIGO and Advanced Virgo gravitational-wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star–black hole binary and a binary black hole, where we show a cross comparison of results obtained using three independent sampling algorithms. These systems were analyzed with nonspinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analyzing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space

Performance Comparison of 3D Sinc Interpolation for fMRI Motion Correction by Language of Implementation and Hardware Platform

Substantial effort is devoted to improving neuroimaging data processing; this effort however, is typically from the algorithmic perspective only. I demonstrate that substantive running time performance improvements to neuroscientific data processing algorithms can be realized by considering their implementation. Focusing specifically on 3D sinc interpolation, an algorithm used for processing functional magnetic resonance imaging (fMRI) data, I compare the performance of Python, C and OpenCL implementations of this algorithm across multiple hardware platforms. I also benchmark the performance of a novel implementation of 3D sinc interpolation on a field programmable gate array (FPGA). Together, these comparisons demonstrate that the performance of a neuroimaging data processing algorithm is significantly impacted by its implementation. I also present a case study demonstrating the practical benefits of improving a neuroscientific data processing algorithm\u27s implementation, then conclude by addressing threats to the validity of the study and discussing future directions

Digital neuromorphic auditory systems

This dissertation presents several digital neuromorphic auditory systems. Neuromorphic systems are capable of running in real-time at a smaller computing cost and consume lower power than on widely available general computers. These auditory systems are considered neuromorphic as they are modelled after computational models of the mammalian auditory pathway and are capable of running on digital hardware, or more specifically on a field-programmable gate array (FPGA). The models introduced are categorised into three parts: a cochlear model, an auditory pitch model, and a functional primary auditory cortical (A1) model. The cochlear model is the primary interface of an input sound signal and transmits the 2D time-frequency representation of the sound to the pitch models as well as to the A1 model. In the pitch model, pitch information is extracted from the sound signal in the form of a fundamental frequency. From the A1 model, timbre information in the form of time-frequency envelope information of the sound signal is extracted. Since the computational auditory models mentioned above are required to be implemented on FPGAs that possess fewer computational resources than general-purpose computers, the algorithms in the models are optimised so that they fit on a single FPGA. The optimisation includes using simplified hardware-implementable signal processing algorithms. Computational resource information of each model on FPGA is extracted to understand the minimum computational resources required to run each model. This information includes the quantity of logic modules, register quantity utilised, and power consumption. Similarity comparisons are also made between the output responses of the computational auditory models on software and hardware using pure tones, chirp signals, frequency-modulated signal, moving ripple signals, and musical signals as input. The limitation of the responses of the models to musical signals at multiple intensity levels is also presented along with the use of an automatic gain control algorithm to alleviate such limitations. With real-world musical signals as their inputs, the responses of the models are also tested using classifiers – the response of the auditory pitch model is used for the classification of monophonic musical notes, and the response of the A1 model is used for the classification of musical instruments with their respective monophonic signals. Classification accuracy results are shown for model output responses on both software and hardware. With the hardware implementable auditory pitch model, the classification score stands at 100% accuracy for musical notes from the 4th and 5th octaves containing 24 classes of notes. With the hardware implementation auditory timbre model, the classification score is 92% accuracy for 12 classes musical instruments. Also presented is the difference in memory requirements of the model output responses on both software and hardware – pitch and timbre responses used for the classification exercises use 24 and 2 times less memory space for hardware than software

2-D coherent integration processing and detecting of aircrafts using GNSS-based passive radar

Long time coherent integration is a vital method for improving the detection ability of global navigation satellite system (GNSS)-based passive radar, because the GNSS signal is not radar-designed and its power level is very low. For aircraft detection, the large range cell migration (RCM) and Doppler frequency migration (DFM) will seriously affect the coherent processing of azimuth signals, and the traditional range match filter will also be mismatched due to the Doppler-intolerant characteristic of GNSS signals. Accordingly, the energy loss of 2-dimensional (2-D) coherent processing is inevitable in traditional methods. In this paper, a novel 2-D coherent integration processing and algorithm for aircraft target detection is proposed. For azimuth processing, a modified Radon Fourier Transform (RFT) with range-walk removal and Doppler rate estimation is performed. In respect to range compression, a modified matched filter with a shifting Doppler is applied. As a result, the signal will be accurately focused in the range-Doppler domain, and a sufficiently high SNR can be obtained for aircraft detection with a moving target detector. Numerical simulations demonstrate that the range-Doppler parameters of an aircraft target can be obtained, and the position and velocity of the aircraft can be estimated accurately by multiple observation geometries due to abundant GNSS resources. The experimental results also illustrate that the blind Doppler sidelobe is suppressed effectively and the proposed algorithm has a good performance even in the presence of Doppler ambiguity