GPU-accelerated LISA parameter estimation with full time domain response (2501.08261v2)

Abstract: We conduct the first full Bayesian inference analysis for LISA parameter estimation incorporating the effects of subdominant harmonics and spin-precession through a full time domain response. The substantial computational demands of using time domain waveforms for LISA are significantly mitigated by implementing a novel Python version of the IMRPhenomT family of waveform models and the LISA response with GPU acceleration. This time domain response alleviates the theoretical necessity of developing specific transfer functions to approximate the LISA response in the Fourier domain for each specific type of system and allows for the use of unequal arms configurations and realistic LISA orbits. Our analysis includes a series of zero-noise injections for a Massive Black Hole Binary with aligned and precessing spins. We investigate the impact of including subdominant harmonics, compare equal and unequal arm configurations, and analyze different Time-Delay-Interferometry (TDI) configurations. We utilize full and uniform priors, with a lower frequency cutoff of 0.1mHz, and a signal duration of approximately two months, sampled every 5 seconds. The sampler is initialized based on Fisher estimates. Our results demonstrate LISA capability to measure the two spin magnitudes and the primary spin tilt angle, alongside sky localization, with percent-level precision, while component masses are determined with sub-percent accuracy.

• The paper introduces a GPU-accelerated Python implementation of IMRPhenomT for full time domain gravitational wave analysis.
• It incorporates realistic LISA configurations with unequal arm lengths, spin precession, and subdominant harmonics for improved modeling.
• The approach achieves percent-level precision in spin measurements and sub-percent accuracy in black hole mass estimates.

Analyzing LISA Parameter Estimation with GPU Acceleration

The paper "GPU-accelerated LISA parameter estimation with full time domain response" investigates a critical aspect of data analysis for the upcoming Laser Interferometer Space Antenna (LISA) mission. This paper marks a significant step in improving the computational efficiency of parameter estimation methods in the paper of gravitational waves (GWs) from massive black hole binaries (MBHBs) by leveraging Graphics Processing Unit (GPU) acceleration.

Summary of Work

The authors have focused on addressing the substantial computational demands inherent in using time domain waveforms for LISA. Traditional methods often relied on Fourier domain approximations due to the computational complexities of directly simulating time domain signals. However, these approximations occasionally compromise the precision and applicability when dealing with diverse and complex systems, such as those exhibiting subdominant harmonics and spin precession.

To overcome these challenges, the team developed a Python implementation of the IMRPhenomT family, tailored for time domain analysis, and integrated GPU acceleration to significantly reduce the computation time. The experiment's framework supports unequal arm configurations and realistic LISA orbits, unlike the conventional equilateral approximations, thereby offering a more accurate representation of the actual conditions expected in space.

Notable Results

The research presents a method where the full Bayesian inference can be executed for gravitational waves, incorporating the effects of subdominant harmonics and spin precession in a time domain framework with satisfactory computational efficiency. Strong numerical performance is emphasized with LISA measuring the spin magnitudes and tilt angles with percent-level precision, while component masses are estimated with sub-percent accuracy.

Particularly, the paper demonstrates the importance of including subdominant harmonics, which significantly improve the parameter estimation precision across several components, including the measurement of individual black hole spins and their precessional dynamics. This enhancement is crucial for decoding astrophysical insights regarding MBHB formation channels.

Theoretical and Practical Implications

The methodological advancements introduced have far-reaching implications. The adoption of GPU-accelerated, time domain approaches in parameter estimation offers superior accuracy compared to traditional methods. This enhancement is critical to making full use of LISA's capabilities, allowing for detailed investigations into complex systems and potentially unearthing new phenomena in gravitational wave astronomy.

Practically, this research supports the premise that future gravitational wave observatories will need to incorporate similar technologies to efficiently process the expected data volumes without losing inference accuracy. The implications here extend beyond LISA and could influence third-generation ground-based detectors as well.

Future Directions

Potential future developments based on this paper could include:

• Further optimization of likelihood evaluation to reduce computational costs even further.
• Expanding the framework to support more complex waveforms incorporating additional physical effects like eccentricity and different instrumental noise models.
• Integration of the methodology within a broader data analysis pipeline for end-to-end simulation accommodating various LISA mission scenarios.

Overall, this research underlines the critical role of technological innovation, particularly GPU acceleration, in advancing the state-of-the-art in gravitational wave astronomy, particularly as it applies to the paper of complex astrophysical systems with space-based detectors like LISA.