The LISA Data Challenges

TL;DR

The paper discusses the LISA Data Challenges, a collaborative effort to develop methods for analyzing complex gravitational wave data from the upcoming space-based LISA detector, including new simulated datasets and analysis strategies.

Contribution

It introduces the Sangria dataset for mild source confusion analysis and outlines the LISA Data Challenges' strategy and tools for future gravitational wave data analysis.

Findings

Introduction of the Sangria dataset for source confusion studies

Outline of the LISA Data Challenges strategy and tools

Preparation for analyzing complex LISA gravitational wave data

Abstract

The future space-based gravitational-wave detector LISA will deliver rich and information-dense data by listening to the milliHertz Universe. The measured time series will contain the imprint of tens of thousands of detectable Galactic binaries constantly emitting, tens of supermassive black hole merger events per year, tens of stellar-origin black holes, and possibly thousands of extreme mass-ratio inspirals. On top of that, we expect to detect the presence of stochastic gravitational wave backgrounds and bursts. Finding and characterizing many such sources is a vast and unsolved task. The LISA Data Challenges (LDCs) are an open and collaborative effort to tackle this exciting problem. A new simulated data set, nicknamed Sangria, has just been released with the purpose of tackling mild source confusion with idealized instrumental noise. This presentation will describe the LDC strategy, showcase the available datasets and analysis tools, and discuss future efforts to prepare LISA data analysis.