Frontier Advances in Global Fitting 
Technology

— by He Wang (ICTP-AP, UCAS)

1. Littenberg, Tyson B., and Neil J. Cornish. “Prototype Global Analysis of LISA Data with Multiple Source Types.” arXiv, January 9, 2023. http://arxiv.org/abs/2301.03673.
2. Strub S H, Ferraioli L, Schmelzbach C, et al. Global analysis of LISA data with Galactic binaries and massive black hole binaries[J]. Physical Review D, 2024, 110(2): 024005.
3. Strub S H. Advancing Global Parameter Estimation of Gravitational Waves Recorded by LISA[D]. ETH Zurich, 2024.
4. Bandopadhyay D, Moore C J. GPU-accelerated semicoherent hierarchical search for stellar-mass binary inspiral signals in LISA[J]. Physical Review D, 2024, 110(10): 103026.
5. Houba N, Strub S H, Ferraioli L, et al. Detection and prediction of future massive black hole mergers with machine learning and truncated waveforms[J]. Physical Review D, 2024, 110(6): 062003.
6. Hoy C, Weaving C R, Nuttall L, et al. A rapid multi-modal parameter estimation technique for LISA[J]. Classical and Quantum Gravity, 2024.
7. Alvey J, Bhardwaj U, Domcke V, et al. Leveraging Time-Dependent Instrumental Noise for LISA SGWB Analysis[J]. arXiv preprint arXiv:2408.00832, 2024.  https://github.com/Mauropieroni/GW_response

8. Karnesis, Nikolaos, Michael L Katz, Natalia Korsakova, Jonathan R Gair, and Nikolaos Stergioulas. “Eryn: A Multipurpose Sampler for Bayesian Inference.” Monthly Notices of the Royal Astronomical Society 526, no. 4 (December 21, 2023): 4814–30. https://doi.org/10.1093/mnras/stad2939.

9. Lackeos K, Littenberg T B, Cornish N J, et al. The LISA Data Challenge Radler analysis and time-dependent ultra-compact binary catalogues[J]. Astronomy & Astrophysics, 2023, 678: A123.

15 minutes (10+5)

• Space-based gravitational wave program and scientific objectives
• What is global fitting, and why is it important?
  • The bridge to science
• The Data Analysis Challenge
• Implementing the Global Fit
• Current Global Fit Implementations
  • 1,2,3,4...
• Future Plans
• Main takeaways

Space-borne​
GW Detector

LISA / TaiJi / TianQin program

The Bridge to Science​

Disentangling All Sources:

Fit all parameters of all astrophysical signals and instrumental features observed simultaneously and comprehensively.

1. Galactic Binaries

• Study the formation and evolution of compact binary stars and the structure of the Milky Way Galaxy

The analysis of the best currently known LISA binaries, even making maximal use of the available information about the sources, is susceptible to ambiguity or biases when not simultaneously fitting to the rest of the galactic population.          (copied from Littenberg et al. 2404.03046)

credit: Karnesis et al, arXiv:2303.02164

credit: Kupfer et al, arXiv:2302.12719

credit: Karnesis et al, arXiv:2303.02164v2

2. Massive Black Holes

• Trace the origins, growth and merger histories of massive Black Holes across cosmic epochs

The addition of GBs biases the parameter recovery of masses and spins away from the injected values, reinforcing the need for a global fit pipeline which will simultaneously fit the parameters of the GB signals before estimating the parameters of MBHBs.  
                          (Copied from Weaving, et al. CQG, 2023, 41(2): 025006.)

3. Extreme and Intermediate Mass Ratio Inspirals

• Probe the properties and immediate environments of Black Holes in the local Universe using extreme mass-ratio inspirals and intermediate mass-ratio inspirals

4. Stellar Mass Black Holes

• Understand the astrophysics of stellar-mass Black Holes

LISA Data Challenge 1b: Yorsh
https://lisa-ldc.lal.in2p3.fr/challenge1b

5. Fundamental Nature of Gravity

• Explore the fundamental nature of gravity and Black Holes

6. Expansion of the Universe

7. Stochastic Gravitational Wave Backgrounds

• Probe the rate of expansion of the Universe with standard sirens

• Understand stochastic gravitational wave backgrounds and their implications for the early Universe and TeV-scale particle physics
   
• Search for gravitational wave bursts and unforeseen sources

Current
Global Fit Implementations

Once all is removed that can be removed, that is how designs are truly in their simplest form.

（Statistics as of December 2024, covering only MBHB and UCB-related. If there are any discrepancies, please let me know.)

（Statistics as of December 2024, covering only MBHB and UCB-related. If there are any discrepancies, please let me know.)

（Statistics as of December 2024, covering only MBHB and UCB-related. If there are any discrepancies, please let me know.)

（Statistics as of December 2024, covering only MBHB and UCB-related. If there are any discrepancies, please let me know.)

（Statistics as of December 2024, covering only MBHB and UCB-related. If there are any discrepancies, please let me know.)

（Statistics as of December 2024, covering only MBHB and UCB-related. If there are any discrepancies, please let me know.)

（Statistics as of December 2024, covering only MBHB and UCB-related. If there are any discrepancies, please let me know.)

• Tempered RJMCMC
• Information build up over time

• Maximum Likelihood Estimate
• Week by week build up
• GPU!

Exact GBs

进动+偏心率

Main takeaways

• Multiple pipelines and their outputs available
• Still preliminary; much remains to be done
• Additional software engineering support and a comprehensive mock data challenge/benchmark are needed.

Future Plans

• Independently developed and implemented
• Python support
• GPU driven
• AI powered

THANK YOU

If you have any questions, feel free to ask now or email me: 

      hewang@ucas.ac.cn