Speaker
Description
The next generation of gravitational wave (GW) interferometers, in particular the Laser Interferometer Space Antenna (LISA) will revolutionize our ability to explore the Universe through Gws. However, there is a significant data analysis challenge that comes with this increased sensitivity. While current ground-based GW detectors are noise dominated with sparse signals due to merging compact objects, the next generation detectors will be signal dominated, with thousands of resolvable binary systems as
well as astrophysical (and possibly cosmological) stochastic gravitational wave backgrounds (SGWBs) within instrument reach. This calls for a paradigm change in the data analysis [1].
Based on exploratory works with J. Alvey and M. Pieroni [2,3] the goal of this project is to build a global fit framework for LISA data analysis based on simulation based inference (SBI). The simulator is modular, containing different GW sources as well as the instrument response. The SBI framework is based on the Truncated Marginal Neural Network Estimation (TMNRE) algorithm. All code will be made publicly available at [https://github.com/peregrine-gw/saqqara].
The exploratory works [2,3] have included instrument noise models, stochastic backgrounds and sub-threshold transient sources, leveraging the key advantages of neural networks over more traditional approaches, in particular the fact that these methods are likelihood-free and the intrinsic marginalisation over nuisance parameters. The goal of this project is to extend this framework to a realistic setup, including all expected relevant GW sources for LISA, and to benchmark and test the pipelines in the LISA Data Challenges.
As members of the LISA consortium, M. Pieroni and V. Domcke will be able to follow up on the implementation of the project results in the data analysis pipelines. J. Alvey, an established young researcher on the field of machine learning techniques in astroparticle physics implementation, will play a key role in coordinating the project.
[1] Cornish, Crowder, Phys. Rev. D (72) 043005 (2005). Littenberg, Cornish, Phys. Rev. D (107) 063001 (2018)
[2] Alvey, Bhardwaj, Domcke, Pieroni, Weniger, Phys. Rev. D (109) 083008 (2024)
[3] Alvey, Bhardwaj, Domcke, Pieroni, Weniger, Phys. Rev. D (111) 102008 (2025)
CERN group/ Experiment
TH
| Working area | Area 1" Cutting Edge AI for Offline Data Processing |
|---|---|
| Project goals | Development of a simulation based inference pipeline for gravitational wave data analysis in LISA (laser interferometer space antenna) |
| Timeline | 1st year: simulation and recovery of dominant signals (supermassive black holes, white dwarfs) in mock data. 2nd year: extend to include subdominant signals: stellar origin black holes, white dwarf confusion noise, stochastic backgrounds. Year 3: Participate in offical LISA Data Challenges. |
| Available person power | 1 staff, 1 fellow plus two external collaborators |
| Additional person power request | 1 fellow |
| Is this an already ongoing activity? | Yes |