SiGMa-Net II: Distinguishing Binary Black Holes from Glitches

TL;DR

This paper introduces an advanced transfer learning approach using InceptionNetV3 and Sine-Gaussian Projection maps to accurately distinguish binary black hole signals from glitches in gravitational wave data, enhancing detection speed and reliability.

Contribution

The work develops a novel method combining SGP maps with transfer learning to improve binary black hole detection amidst noise glitches, outperforming previous CNN-based approaches.

Findings

Achieved 87% accuracy in distinguishing BBHs from glitches.

Maintained high accuracy on real LIGO BBH events.

Demonstrated robustness of the method across different noise conditions.

Abstract

With increasing sensitivity of the gravitational wave (GW) detectors, we expect a significant rise in the detectable GW events. To process, analyse and identify such large amounts of GW signals arising from mergers of Binary Black Holes (BBH), we need both speed and accuracy. In the search for (massive) BBH signals, the biggest hurdle is posed by the various non-gaussian noise transients called glitches. Compared to our previous work, which used a simple convolutional neural network to distinguish BBHs from Blip glitches, this work uses transfer learning with InceptionNetV3 to distinguish BBHs from six types of most popular glitches from the third observing run of LIGO. While the glitches are real and identified via GravitySpy, the BBH signals are simulated and then injected into the real detector noise for each of the two LIGO detectors. We generate Sine-Gaussian Projection (SGP) maps by cross-correlating data with Sine-Gaussian functions of varied quality factors ($Q$) and central frequencies ($f_0$) and projected on the $Q$ - $f_0$ plane. We find that SGP maps make it easier to distinguish BBHs from glitches that look very similar to BBHs in the Time-Frequency maps like the Blips, while also maintaining significant morphological differences between BBHs and the more frequent glitches - Scattered Light and Fast Scattering. Our network has an accuracy of $87%$, a TPR of 0.83 for an FPR of 0.1 on our test dataset. It is also robust, retaining its level of accuracy, when tested on real BBH events identified in the first three observing runs of LIGO. Our proposed method shows the viability of using the SGP maps and neural networks for fast identification of GW events improving the efficiency of standard search pipelines.