The formation mechanism of supermassive black holes is yet unknown, despite their presence in nearly every galaxy, including the Milky Way. As stellar evolution predicts that stars cannot collapse to black holes $\gtrsim 50 - 130 M_{\odot}$ due to pair-instability, plausible formation mechanisms include the hierarchical mergers of intermediate-mass black holes (IMBHs). The direct observation...
We present Conditional Derivative GAN (cDVGAN), a novel conditional GAN framework for simulating multiple classes of gravitational wave (GW) transients in the time domain. cDVGAN can also generate generalized hybrid waveforms that span the variation between waveform classes through class-interpolation in the conditioned class vector. cDVGAN transforms the typical 2-player adversarial game of...
After identifying a gravitational wave, the goal of parameter estimation pipelines is to infer the parameters of the source that generated the signal. Current methods rely on computationally expensive numerical approaches, such as Markov chain Monte Carlo (MCMC) samplers. For longer signals with a high-dimensional parameter space, such as gravitational waves generated by binary neutron star...
We present an initial approach to applying a quantum support vector machine (QSVM) to the detection of gravitational waves. We explore the effect of the variation of the hyperparameters associated with quantum computing on the detection rate and compare the results with a classical support vector machine (SVM). The training and testing dataset is generated by injecting simulated events into...
Modern simulation-based inference techniques leverage neural networks to solve inverse problems efficiently. One notable strategy is neural posterior estimation (NPE), wherein a neural network parameterizes a distribution to approximate the posterior. This approach is particularly advantageous for tackling low-latency or high-volume inverse problems. However, the accuracy of NPE varies...
Because of its speed after training, machine learning is often envisaged as a solution to a manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been given for various applications in gravitational-wave data analysis. In this Letter, we focus on a challenging problem faced by third-generation detectors: parameter inference for overlapping signals. Because of the...
The current and upcoming generations of gravitational wave experiments represent an exciting step forward in terms of detector sensitivity and performance. Key upgrades at the LIGO, Virgo and KAGRA facilities will see the next observing run (O4) probe a spatial volume around four times larger than the previous run (O3), and design implementations for e.g. the Einstein Telescope, Cosmic...
In some sense, the detection of a stochastic gravitational wave background (SGWB) is one of the most subtle GW analysis challenges facing the community in the next-generation detector era. For example, at an experiment such as LISA, to extract the SGWB contributions, we must simultaneously: detect and analyse thousands of highly overlapping sources including massive binary black holes mergers...
