Orateur
Description
This work focuses on data analysis for LISA, with a particular focus on fast parameter inference of massive black hole binaries (MBHBs). Fast parameter estimation will be essential for LISA’s Low-Latency Alert Pipeline (LLAP), which will enable electromagnetic and multi-messenger follow-up of detected sources. This requires working with pre-merger signals, allowing other observatories to see the detected events thanks to the alerts given by the pipeline. These constraints and the high computational cost of standard Bayesian methods motivate the use of amortized approaches such as simulation-based inference (SBI).
We present a modular SBI pipeline for MBHB parameter estimation using LISA time-domain data. It includes dataset generation with a fast response that we developed, training of a neural posterior estimator, refinement of the sampling results with importance sampling, and posterior validation. The analysis is performed directly on time-domain signals to avoid pre-processing of the received data. The modularity of the pipeline allows us to independently test and improve each of its components.
We explore several approaches to improve the accuracy and efficiency of the inference, including learned summary statistics to compress the time-domain signals, restricting the priors based on a detection signal-to-noise ratio threshold, and testing other models such as flow matching. We also work on global standardization of the parameters instead of batch standardization, in order to better handle the large range of parameter values.