Orateur
Description
LISA will observe massive black hole binaries (MBHBs) hours to weeks before they merge. If these systems are in gas-rich environments, they might produce an electromagnetic (EM) counterpart: this motivates the development of pipelines able to localize and characterize the source fast enough to trigger a follow-up search. The main data analysis pipeline, the Global Fit, will model all LISA sources jointly, which makes it too slow for near-real-time alerts. I will present two approaches for quick detection and parameter estimation of MBHBs.
The first is a simulation-based inference approach that employs conditional flow matching with optimal transport: we train a network to learn the trajectory from a base distribution to the parameters' posteriors, conditioning on data. The computational cost is shifted to the training phase, allowing the trained model to produce posteriors within low-latency requirements. We test the approach on both time and time-frequency representations of the data, and project our data into a space where other sources are ignored, by training autoencoders to remove the galactic binary foreground.
The second, in collaboration with Aryamann Rao (UBC), is a detection pipeline that could provide starting points for deeper analyses: through excess power in the Q-transform, a time-frequency representation of the data, we identify candidate signals. We give a first estimate of chirp mass and coalescence time and, based on this trigger, we select which network to run between several trained CNNs, each specialized on a restricted mass range. We provide a quick point estimate of the full set of parameters. We have used this approach to detect sources in the Mojito Light data challenge, and we are now testing the CNNs performance on those detections.