Speaker
Description
Accurate classification of instrumental and environmental noise glitches is essential for gravitational-wave detector characterisation and data quality assurance in LIGO. We present GravCloud, a hybrid deep-learning framework that interprets one-dimensional transient noise burst glitches transformed into a multi-dimensional pointcloud representation, allowing noise transients to be analysed in a richer (geometric) space. The architecture combines Long Short-Term Memory (LSTM) networks for temporal feature extraction with Dynamic Graph Convolutional Neural Networks (DGCNN) for learning spatial relationships of the transient noise parameters represented in a higher dimensional plane. This approach is intended to leverage existing multi-dimensional structural learning tools to capture complex glitch characteristics more compute effectively than conventional one-dimensional or even image-based methods. By moving glitch classification into a higher-dimensional feature space, we aim to improve separation between classes exhibiting subtle or overlapping signatures in any particular dimensional plane, thereby contributing to more reliable automated noise classification and detector monitoring in gravitational-wave experiments.