Exploring a search for long-duration transient gravitational waves associated with magnetar bursts

Soft gamma repeaters and anomalous X-ray pulsars are thought to be magnetars, neutron stars with strong magnetic fields of order $\mathord{\sim} 10^{13}$--$10^{15} \, \mathrm{gauss}$. These objects emit intermittent bursts of hard X-rays and soft gamma rays. Quasiperiodic oscillations in the X-ray tails of giant flares imply the existence of neutron star oscillation modes which could emit gravitational waves powered by the magnetar's magnetic energy reservoir. We describe a method to search for transient gravitational-wave signals associated with magnetar bursts with durations of 10s to 1000s of seconds. The sensitivity of this method is estimated by adding simulated waveforms to data from the sixth science run of Laser Interferometer Gravitational-wave Observatory (LIGO). We find a search sensitivity in terms of the root sum square strain amplitude of $h_{\mathrm{rss}} = 1.3 \times 10^{-21} \, \mathrm{Hz}^{-1/2}$ for a half sine-Gaussian waveform with a central frequency $f_0 = 150 \, \mathrm{Hz}$ and a characteristic time $\tau = 400 \, \mathrm{s}$. This corresponds to a gravitational wave energy of $E_{\mathrm{GW}} = 4.3 \times 10^{46} \, \mathrm{erg}$, the same order of magnitude as the 2004 giant flare which had an estimated electromagnetic energy of $E_{\mathrm{EM}} = \mathord{\sim} 1.7 \times 10^{46} (d/ 8.7 \, \mathrm{kpc})^2 \, \mathrm{erg}$, where $d$ is the distance to SGR 1806-20. We present an extrapolation of these results to Advanced LIGO, estimating a sensitivity to a gravitational wave energy of $E_{\mathrm{GW}} = 3.2 \times 10^{43} \, \mathrm{erg}$ for a magnetar at a distance of $1.6 \, \mathrm{kpc}$. These results suggest this search method can probe significantly below the energy budgets for magnetar burst emission mechanisms such as crust cracking and hydrodynamic deformation

Deep Multi-view Models for Glitch Classification

Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this paper, we propose a deep multi-view convolutional neural network to classify glitches automatically. The primary purpose of classifying glitches is to understand their characteristics and origin, which facilitates their removal from the data or from the detector entirely. We visualize glitches as spectrograms and leverage the state-of-the-art image classification techniques in our model. The suggested classifier is a multi-view deep neural network that exploits four different views for classification. The experimental results demonstrate that the proposed model improves the overall accuracy of the classification compared to traditional single view algorithms.Comment: Accepted to the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'17

Optimization of the configuration and working fluid for a micro heat pipe thermal control device

Continued development of highly compact and powerful electronic components has led to the need for a simple and effective method for controlling the thermal characteristics of these devices. One proposed method for thermal control involves the use of a micro heat pipe system containing a working fluid with physical properties having been speciffcally selected such that the heat pipes, as a whole, vary in effective thermal conductance, thereby providing a level of temperature regulation. To further explore this possibility, a design scenario with appropriate constraints was established and a model developed to solve for the effective thermal conductance of individual heat pipes as a function of evaporator-end temperature. From the results of this analysis, several working fluids were identified and selected from a list over thirteen hundred that were initially analyzed. Next, a thermal circuit model was developed that translated the individual heat pipe operating characteristics into the system as a whole to determine the system level effects. It was found that none of the prospective fluids could completely satisfy the established design requirements to regulate the device temperature over the entire range of operating conditions. This failure to fully satisfy design requirements was due, in large part, to the highly constrained nature of problem definition. Several fluids, however, did provide for an improved level of thermal control when compared to the unmodified design. Suggestions for improvements that may lead to enhanced levels of thermal control are offered as well as areas that are in need of further research

Gravity Spy and X-Pypeline: A multidisciplinary approach to characterizing and understanding non-astrophysical gravitational wave data and its impact on searches for unmodelled signals

With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) has initiated a new field of astronomy by providing an alternate means of sensing the Universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of aLIGO from non-gravitational-wave disturbances. Nonetheless, aLIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate that the possibility of accidental coincidence between the two aLIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are allowed by the design sensitivity of aLIGO. For this reason, over the past few years, glitch classification techniques have been developed to help make this task easier. Specifically, I explore the effect of glitches, and their suppression, on key gravitational-wave searches such as that for a Galactic supernova. Moreover, I explore the impact of including machine learning techniques in the post-processing stage of the gravitational-wave search algorithm, “X-Pypeline”. When performing a two detector network search for a gravitational wave from a Galactic supernova, this thesis finds that including information about glitch families and using machine learning techniques in the post-processing stages of the analysis can improve the sensitive range of the search by 10-15 percent over the standard post-processing method