Sarah Caudill

Current searches for gravitational waves (GWs) from black hole binaries using the LIGO and Virgo observatories are limited to analytical models for systems with black hole spins aligned (or antialigned) with the orbital angular momentum of the binary. Detecting black hole binaries with precessing spins (spins not aligned or antialigned with the orbital angular momentum) is crucial for gaining unique astrophysical insights into the formation of these sources. Therefore, it is essential to develop a search strategy capable of identifying compact binaries with precessing spins. Aligned-spin waveform models are inadequate for detecting compact binaries with high precessing spins. While several efforts have been made to construct template banks for detecting precessing binaries using matched filtering, this approach requires many templates to cover the entire search parameter space, significantly increasing the computational cost. This work explores the detection of GW signals from binary black holes (BBH) with both aligned and precessing spins using a convolutional neural network (CNN). We frame the detection of GW signals from aligned or precessing BBH systems as a hierarchical binary classification problem. The first CNN model classifies strain data as either pure noise or noisy signals (GWs from BBH). A second CNN model then classifies the detected noisy signal data as originating from either precessing or nonprecessing (aligned/antialigned) systems. Using simulated data, the trained classifier distinguishes between noise and noisy GW signals with more than 99% accuracy. The second classifier further differentiates between aligned and highly precessing signals with around 95% accuracy. We extended our analysis to a multidetector framework by performing a coincident test. Additionally, we tested the performance of our trained architecture on data from the first three observation runs (O1, O2, and O3) of LIGO and Virgo to identify detected BBH events as either aligned or precessing.

We introduce a novel method to generate a bank of gravitational-waveform templates of binary black hole (BBH) mergers for matched-filter searches in LIGO, Virgo, and Kagra data. We derive a novel expression for the metric approximation to the distance between templates, which is suitable for precessing BBHs and/or systems with higher-order modes (HM) imprints and we use it to meaningfully define a template probability density across the parameter space. We employ a masked autoregressive normalizing flow model which can be conveniently trained to quickly reproduce the target probability distribution and sample templates from it. Thanks to the normalizing flow, our code takes a few hours to produce random template banks with millions of templates, making it particularly suitable for high-dimensional spaces, such as those associated to precession, eccentricity and/or HM. After validating the performance of our method, we generate a bank for precessing black holes and a bank for aligned-spin binaries with HMs: with only 5% of the injections with fitting factor below the target of 0.97, we show that both banks cover satisfactorily the space. Our publicly released code mbank will enable searches of high-dimensional regions of BBH signal space, hitherto unfeasible due to the prohibitive cost of bank generation.

We introduce a novel method to generate a bank of gravitational-waveform templates of binary black hole (BBH) mergers for matched-filter searches in LIGO, Virgo and Kagra data.We derive a novel expression for the metric approximation to the distance between templates, which is suitable for precessing BBHs and/or systems with higher-order modes (HM) imprints and we use it to meaningfully define a template probability density across the parameter space. We employ a masked autoregressive normalizing flow model which can be conveniently trained to quickly reproduce the target probability distribution and sample templates from it. Thanks to the normalizing flow, our code takes a few {\it hours} to produce random template banks with millions of templates, making it particularly suitable for high-dimensional spaces, such as those associated to precession, eccentricity and/or HM. After validating the performance of our method, we generate a bank for precessing black holes and a bank for aligned-spin binaries with HMs: with only 5% of the injections with fitting factor below the target of 0.97, we show that both banks cover satisfactorily the space. Our publicly released code mbank will enable searches of high-dimensional regions of BBH signal space, hitherto unfeasible due to the prohibitive cost of bank generation.

Our local motion with respect to the cosmic frame of rest is believed to be dominantly responsible for the observed dipole anisotropy in the Cosmic Microwave Background Radiation (CMBR). We study the effect of this motion on the sky distribution of gravitational wave (GW) sources. We determine the resulting dipole anisotropy in GW source number counts, mass weighted number counts, which we refer to as mass intensity, and mean mass per source. The mass M dependence of the number density n(M) distribution of BBH is taken directly from the data. We also test the anisotropy in the observable mean mass per source along the direction of the CMB dipole. The current data sample is relatively small and consistent with isotropy. The number of sources required for this test is likely to become available in near future.

Our local motion with respect to the cosmic frame of rest is believed to be dominantly responsible for the observed dipole anisotropy in the Cosmic Microwave Background Radiation (CMBR). We study the effect of this motion on the sky distribution of gravitational wave (GW) sources. We determine the resulting dipole anisotropy in GW source number counts, mass weighted number counts, which we refer to as mass intensity, and mean mass per source. The mass M dependence of the number density n(M) distribution of BBH is taken directly from the data. We also test the anisotropy in the observable mean mass per source along the direction of the CMB dipole. The current data sample is relatively small and consistent with isotropy. The number of sources required for this test is likely to become available in the near future.