A rapid multi-modal parameter estimation technique for LISA

Abstract The laser interferometer space antenna (LISA) will observe gravitational-wave (GW) signals from a wide range of sources, including massive black hole binaries (MBHBs). Although numerous techniques have been developed to perform Bayesian inference for LISA, they are often computationally expensive; analyses often take at least ∼1 month on a single CPU, even when using accelerated techniques. Not only does this make it difficult to concurrently analyse more than one GW signal, it also makes it challenging to rapidly produce parameter estimates for possible electromagnetic follow-up campaigns. simple-pe was recently developed to produce rapid parameter estimates for GW signals observed with ground-based GW detectors. In this work, we extend simple-pe to produce rapid parameter estimates for LISA sources, including the effects of higher order multipole moments. We show that simple-pe infers the source properties of massive black hole binaries in zero-noise at least <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mrow><mml:mrow><mml:mo>∼</mml:mo></mml:mrow><mml:mn>100</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math> faster than existing techniques; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mrow><mml:mrow><mml:mo>∼</mml:mo></mml:mrow><mml:mn>12</mml:mn></mml:mrow></mml:math> h on a single CPU. We further demonstrate that simple-pe can be applied before existing Bayesian techniques to mitigate biases in multi-modal parameter estimation analyses of MBHBs.

Authors

Institutions

Publication Details

Journal
Classical and Quantum Gravity
Published
2024-11-05
DOI
https://doi.org/10.1088/1361-6382/ad8f26
Citations
5
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
1.74

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A rapid multi-modal parameter estimation technique for LISA

C. R. Weaving, C. G. Hoy, I. W. Harry, L. K. Nuttall
5 citations
Classical and Quantum Gravity
Computational Physics and Python Applications
1.74
article

A rapid multi-modal parameter estimation technique for LISA

C. R. Weaving, C. G. Hoy, I. W. Harry, L. K. Nuttall
article en
5 citations

Abstract

Abstract The laser interferometer space antenna (LISA) will observe gravitational-wave (GW) signals from a wide range of sources, including massive black hole binaries (MBHBs). Although numerous techniques have been developed to perform Bayesian inference for LISA, they are often computationally expensive; analyses often take at least ∼1 month on a single CPU, even when using accelerated techniques. Not only does this make it difficult to concurrently analyse more than one GW signal, it also makes it challenging to rapidly produce parameter estimates for possible electromagnetic follow-up campaigns. simple-pe was recently developed to produce rapid parameter estimates for GW signals observed with ground-based GW detectors. In this work, we extend simple-pe to produce rapid parameter estimates for LISA sources, including the effects of higher order multipole moments. We show that simple-pe infers the source properties of massive black hole binaries in zero-noise at least <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mrow><mml:mrow><mml:mo>∼</mml:mo></mml:mrow><mml:mn>100</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math> faster than existing techniques; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mrow><mml:mrow><mml:mo>∼</mml:mo></mml:mrow><mml:mn>12</mml:mn></mml:mrow></mml:math> h on a single CPU. We further demonstrate that simple-pe can be applied before existing Bayesian techniques to mitigate biases in multi-modal parameter estimation analyses of MBHBs.

Classical and Quantum GravityVol. 41(24)
University of Portsmouth (GB)
UK Research and Innovation, Science and Technology Facilities Council
Openalex Percentile: Top 13%
Computational Physics and Python Applications
1.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.