arXiv subjects

C. J. Moore

Publications and source records attributed to C. J. Moore.

5 recordsLinked to original sources

arXiv2026

Improved Automatic Target Recognition in Synthetic Aperture Sonar Imagery Using Large Deep Neural Networks

Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) is a task largely dominated by deep neural networks (DNNs). Most SAS-ATR models use convolutional neural network (CNN) architectures whereas transformer-based architectures have had much less representation in the literature despite being state of the art in general computer vision (CV) research. Additionally, researchers have had mixed results in attempting to overcome challenges presented by a scarcity of labeled training data by using methods such as data augmentation and the use of pretrained weights from a variety of imaging modalities. In this work, we compare the performance of modern CNN and transformer-based DNNs to determine which architecture and training configurations elicit the highest performance in SAS-ATR. We investigate how network size, architecture, pretraining method, data augmentation and other forms of regularization affect SAS-ATR performance with a focus on producing the highest-performing model and providing a roadmap for training state-of-the-art SAS-ATR models.

cs.CV↗

arXiv2026

A Comparison of Data Augmentation Methods for Training Deep Neural Networks on Synthetic Aperture Sonar

In this work we study Automatic Target Recognition (ATR) for Synthetic Aperture Sonar (SAS) data with a focus on deep neural networks (DNNs). The main challenge in training DNNs for SAS-ATR arises from the limited quantity of labeled target examples due to the significant costs and time required to collect real-world SAS data. One successful general strategy for mitigating the problem of limited training data is augmentation, which generates additional synthetic training data by introducing realistic variations to available data. Prior research has investigated a variety of augmentation strategies for SAS-ATR, including conventional image augmentations (e.g., contrast changes, cropping) as well as augmentations motivated the specific physics of SAS data. Building on prior work, we systematically compare many of these existing augmentation strategies for training DNNs for SAS-ATR. We also investigate the impact of augmentation when combined with modern DNN architectures such as transformers. The results indicate that augmentation can improve target recognition accuracy, although benefits vary, and not all augmentations are beneficial.

cs.LG↗

arXiv2022

A Kilonova Following a Long-Duration Gamma-Ray Burst at 350 Mpc

Here, we report the discovery of a kilonova associated with the nearby (350 Mpc) minute-duration GRB 211211A. In tandem with deep optical limits that rule out the presence of an accompanying supernova to $M_I > -13$ mag at 17.7 days post-burst, the identification of a kilonova confirms that this burst's progenitor was a compact object merger. While the spectrally softer tail in GRB 211211A's gamma-ray light curve is reminiscent of previous extended emission short GRBs (EE-SGRBs), its prompt, bright spikes last $\gtrsim 12$ s, separating it from past EE-SGRBs. GRB 211211A's kilonova has a similar luminosity, duration and color to AT2017gfo, the kilonova found in association with the gravitational wave (GW)-detected binary neutron star (BNS) merger GW170817. We find that the merger ejected $\approx 0.04 M_{\odot}$ of r-process-rich material, and is consistent with the merger of two neutron stars (NSs) with masses close to the canonical $1.4 M_{\odot}$. This discovery implies that GRBs with long, complex light curves can be spawned from compact object merger events and that a population of kilonovae following GRBs with durations $\gg 2$ s should be accounted for in calculations of the NS merger r-process contribution and rate. At 350 Mpc, the current network of GW interferometers at design sensitivity would have detected the merger precipitating GRB 211211A, had it been operating at the time of the event. Further searches for GW signals coincident with long GRBs are therefore a promising route for future multi-messenger astronomy.

astro-ph.HE↗

arXiv2020

Populations of double white dwarfs in Milky Way satellites and their detectability with LISA

Milky Way dwarf satellites are unique objects that encode the early structure formation and therefore represent a window into the high redshift Universe. So far, their study was conducted using electromagnetic waves only. The future Laser Interferometer Space Antenna (LISA) has the potential to reveal Milky Way satellites in gravitational waves emitted by double white dwarf (DWD) binaries. We investigate gravitational wave (GW) signals detectable by LISA as a possible tool for the identification and characterisation of the Milky Way satellites. We use the binary population synthesis technique to model the population of DWDs in dwarf satellites and we assess the impact on the number of LISA detections when making changes to the total stellar mass, distance, star formation history and metallicity of satellites. We calibrate predictions for the known Milky Way satellites on their observed properties. We find that DWDs emitting at frequencies $\gtrsim 3\,$mHz can be detected in Milky Way satellites at large galactocentric distances. The number of these high frequency DWDs per satellite primarily depends on its mass, distance, age and star formation history, and only mildly depends on the other assumptions regarding their evolution such as metallicity. We find that dwarf galaxies with $M_\star>10^6\,$M$_{\odot}$ can host detectable LISA sources with a number of detections that scales linearly with the satellite's mass. We forecast that out of the known satellites, Sagittarius, Fornax, Sculptor and the Magellanic Clouds can be detected with LISA. As an all-sky survey that does not suffer from contamination and dust extinction, LISA will provide observations of the Milky Way and dwarf satellites galaxies valuable for Galactic archaeology and near-field cosmology.

astro-ph.GA↗

arXiv2018

Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers

We present $\texttt{ENIGMA}$, a time domain, inspiral-merger-ringdown waveform model that describes non-spinning binary black holes systems that evolve on moderately eccentric orbits. The inspiral evolution is described using a consistent combination of post-Newtonian theory, self-force and black hole perturbation theory. Assuming eccentric binaries that circularize prior to coalescence, we smoothly match the eccentric inspiral with a stand-alone, quasi-circular merger, which is constructed using machine learning algorithms that are trained with quasi-circular numerical relativity waveforms. We show that $\texttt{ENIGMA}$ reproduces with excellent accuracy the dynamics of quasi-circular compact binaries. We validate $\texttt{ENIGMA}$ using a set of $\texttt{Einstein Toolkit}$ eccentric numerical relativity waveforms, which describe eccentric binary black hole mergers with mass-ratios between $1 \leq q \leq 5.5$, and eccentricities $e_0 \lesssim 0.2$ ten orbits before merger. We use this model to explore in detail the physics that can be extracted with moderately eccentric, non-spinning binary black hole mergers. We use $\texttt{ENIGMA}$ to show that GW150914, GW151226, GW170104, GW170814 and GW170608 can be effectively recovered with spinning, quasi-circular templates if the eccentricity of these events at a gravitational wave frequency of 10Hz satisfies $e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175,\, 0.125\}$, respectively. We show that if these systems have eccentricities $e_0\sim 0.1$ at a gravitational wave frequency of 10Hz, they can be misclassified as quasi-circular binaries due to parameter space degeneracies between eccentricity and spin corrections. Using our catalog of eccentric numerical relativity simulations, we discuss the importance of including higher-order waveform multipoles in gravitational wave searches of eccentric binary black hole mergers.

gr-qc↗