Speaker
Description
As next-generation observatories push the boundaries of sensitivity and data throughput, the challenge of isolating faint, unmodeled astrophysical transients from non-Gaussian terrestrial noise has become a universal bottleneck. Both radio astronomy and gravitational-wave (GW) physics face highly analogous data environments: continuous, high-rate time-series streams plagued by instrumental glitches and interference that easily mimic true signals.
This presentation outlines a mature, edge-deployable machine learning architecture originally developed for the real-time detection of Fast Radio Bursts (FRBs) at remote outrigger stations, and maps its direct translatability to GW low-latency pipelines. We detail a Multi-Input Convolutional Neural Network (CNN) designed to overcome the limitations of single-domain representation. Rather than relying solely on a 2D spectrogram, our architecture utilizes a branched topology to concurrently extract features from three distinct physical representations: the 1D integrated time profile, the 1D bandpass spectrum, and the 2D temporal-spectral waterfall plot.
By forcing the network's dense decision layers to cross-reference multi-modal physics, the pipeline achieves exceptional robustness, drastically suppressing false-positive rates caused by localized terrestrial interference (which typically mimics a transient in only one domain). Furthermore, we discuss the coupling of this multi-branch CNN with Just-In-Time (JIT) compiled streaming buffers, ensuring the inference latency remains well within the strict bounds required for automated, multi-messenger follow-up prioritization. By bridging the data analysis methodologies of radio and GW astronomy, we present a scalable framework for unmodeled search representation learning, directly applicable to the noise-hunting and transient detection challenges of the LIGO-Virgo-KAGRA network and the upcoming Einstein Telescope.