Speaker
Description
With the LISA mission formally adopted by ESA in January 2024 and now in its implementation phase, preparations across the science ground segment are accelerating toward launch in the mid‑2030s. A central priority is ensuring that data‑analysis tools are ready to extract science quickly and reliably once telemetry becomes available.
Within this context, here we present GWEEP(Gravitational Wave DEEp-learning Pipeline), a deep‑learning toolkit designed for rapid detection and parameter estimation of gravitational‑wave signals.
GWEEP combines efficient neural architectures with domain‑specific pre‑processing to operate on streaming batches, enabling low‑latency triage of candidate transients and early characterisation of their source parameters.
We illustrate the pipeline design and summarise performance on recent LISA‑like datasets. For validation purposes we’ve used the LISA data challenges, a set of realistic LISA mock data prepared and launched periodically by LISA LDC group that offered the perfect environment to develop and test the data processing tools within the Consortium.
We conclude by outlining the roadmap for deployment within the consortium’s data‑processing ecosystem.