arXiv subjects

Gregory Baltus

Publications and source records attributed to Gregory Baltus.

3 recordsLinked to original sources

arXiv2024

Improving early detection of gravitational waves from binary neutron stars using CNNs and FPGAs

The detection of gravitational waves (GWs) from binary neutron stars (BNSs) with possible telescope follow-ups opens a window to ground-breaking discoveries in the field of multi-messenger astronomy. With the improved sensitivity of current and future GW detectors, more BNS detections are expected in the future. Therefore, enhancing low-latency GW search algorithms to achieve rapid speed, high accuracy, and low computational cost is essential. One innovative solution to reduce latency is the use of machine learning (ML) methods embedded in field-programmable gate arrays (FPGAs). In this work, we present a novel \texttt{WaveNet}-based method, leveraging the state-of-the-art ML model, to produce early-warning alerts for BNS systems. Using simulated GW signals embedded in Gaussian noise from the Advanced LIGO and Advanced Virgo detectors' third observing run (O3) as a proof-of-concept dataset, we demonstrate significant performance improvements. Compared to the current leading ML-based early-warning system, our approach enhances detection accuracy from 66.81\% to 76.22\% at a 1\% false alarm probability. Furthermore, we evaluate the time, energy, and economical cost of our model across CPU, GPU, and FPGA platforms, showcasing its potential for deployment in real-time gravitational wave detection pipelines.

astro-ph.IM↗

arXiv2021

Search for sub-solar primordial black holes in low mass ratio binaries with LIGO-Virgo O2 data and implications for the primordial black hole dark matter fraction

We perform a search for binary black hole mergers with one sub-solar mass (SSM) black hole and a primary component above $\sim 2 M_\odot$ in data from the second observing run (O2) of the LIGO-Virgo detectors. Our analysis extends the parameter space explored by previous LIGO-Virgo Collaboration searches for binaries containing SSM components into a region of parameter space motivated by broad mass distributions of primordial black holes (PBHs) exhibiting a peak around $[2-3] M^{}_\odot$, which can arise from the reduction of the equation of state during the QCD phase transition in the early Universe. Four candidate events are found passing a signal-to-noise ratio (SNR) threshold of 8 and a false alarm rate (FAR) threshold of 2 per year, although none are statistically significant enough to constitute a confident detection. Assuming a null result for the search, we derive PBH model-independent 90\% confidence upper limits on the PBH merger rates by estimating the sensitive volume-time of the search using simulated gravitational-wave signal injections. We interpret these observational limits using a representative broad PBH mass function bearing imprints of the thermal history of the early Universe and considering both early and late PBH binary formation channels. The resulting constraints on the PBH dark-matter fraction, $f^{}_{\rm PBH}$, depend on the assumed mass function and merger-rate prescription. For all considerations, the upper limits remain above $f^{}_{\rm PBH}=1$, indicating the O2 data from LIGO-Virgo are not sensitive enough to place meaningful constraints within the assumptions of the PBH mass model and PBH binary mergers.

astro-ph.CO↗

arXiv2021

Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal

GW170817 has led to the first example of multi-messenger astronomy with observations from gravitational wave interferometers and electromagnetic telescopes combined to characterise the source. However, detections of the early inspiral phase by the gravitational wave detectors would allow the observation of the earlier stages of the merger in the electromagnetic band, improving multi-messenger astronomy and giving access to new information. In this paper, we introduce a new machine-learning-based approach to produce early-warning alerts for an inspiraling binary neutron star system, based only on the early inspiral part of the signal. We give a proof of concept to show the possibility to use a combination of small convolutional neural networks trained on the whitened detector strain in the time domain to detect and classify early inspirals. Each of those is targeting a specific range of chirp masses dividing the binary neutron star category into three sub-classes: light, intermediate and heavy. In this work, we focus on one LIGO detector at design sensitivity and generate noise from the design power spectral density. We show that within this setup it is possible to produce an early alert up to 100 seconds before the merger for the best-case scenario. We also present some future upgrades that will enhance the detection capabilities of our convolutional neural networks. Finally, we also show that the current number of detections for a realistic binary neutron star population is comparable to that of matched filtering and that there is a high probability to detect GW170817- and GW190425-like events at design sensitivity.

gr-qc↗