Ana Isabel Silva Martins

• Lopez, Melissa; Santoro, Giada Caneva; Martins, Ana Isabel Silva; Schmidt, Stefano; Schoppink, Jonno & Straalen, Wouter Van [Show all 8 contributors for this article] (2025). Ameliorating transient noise bursts in gravitational-wave searches for intermediate-mass black holes. Physical Review D. ISSN 2470-0010. 111(10). doi: 10.1103/PhysRevD.111.103020. Full text in Research Archive Show summary

  The direct observation of intermediate-mass black holes (IMBH) populations would not only strengthen the possible evolutionary link between stellar and supermassive black holes, but unveil the details of the pair-instability mechanism and elucidate their influence in galaxy formation. Conclusive observation of IMBHs remained elusive until the detection of gravitational-wave (GW) signal GW190521, which lies with high confidence in the mass gap predicted by the pair-instability mechanism. Despite falling in the sensitivity band of current GW detectors, IMBH searches are challenging due to their similarity to transient bursts of detector noise, known as glitches. In this proof-of-concept work, we combine a matched-filter algorithm with a machine learning (ML) method to differentiate IMBH signals from nontransient burst noise, known as glitches. In particular, we build a multilayer perceptron network to perform a multiclass classification of the output triggers of matched filter. In this way we are able to distinguish simulated GW IMBH signals from different classes of glitches that occurred during the third observing run (O3) in single detector data. We train, validate, and test our model on O3a data, reaching a true positive rate of over 90% for simulated IMBH signals. To test the generalization ability over the evolutionary observing run, we test on the unseen data of O3b, which yields a true positive rate of over 70%. We also combine data from multiple detectors to search for simulated IMBH signals in real detector noise, providing a significance measure for the output of our ML method.

• Martins, Ana Isabel Silva; Lopez, Melissa; Baltus, Gregory; Meijer, Quirijn; Sluys, Marc van der & Broeck, Chris Van Den [Show all 7 contributors for this article] (2025). Improving early detection of gravitational waves from binary neutron stars using CNNs and FPGAs. Machine Learning: Science and Technology. 6(1). doi: 10.1088/2632-2153/adbf66. Full text in Research Archive Show summary

  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 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 GW detection pipelines.

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