Frontiers of AI in Gravitational Wave Astronomy

Pioneering works utilizing CNN

• The most common and direct approach, from Computer Vision (CV) to GW signal processing: pixel point $\Rightarrow$ sampling point.

• Convolutional neural networks (CNN) can achieve comparable performance to Matched Filtering and surpass them in terms of execution speed (with GPU support) under Gaussian stationary noise.

AI for Science $\rightarrow$ AI for GW Astronomy






 

• Artificial Intelligence (AI) has great potential to revolutionize gravitational wave astronomy by improving data analysis, modeling, and detector development.
• Representation and supervised learning crucially extract features from GW signals, autonomously identifying informative features and leveraging labeled data for accuracy.

 Introduction to Speed and Efficiency

• Machine Learning (ML) offers unparalleled speed in GW detection.
• The increasing sensitivity to noise and the growing number of GW events demand faster analysis capabilities.
   

The Need for Integration (an AI pipeline!)

• Numerous AI algorithms exist for signal detection (see more: https://wiki.ligo.org/MLA/ML_at_LIGO_and_VIRGO), highlighting the need for a streamlined pipeline.
• However, none are operational in O4 due to infrastructure challenges and the lack of standardized sensitivity measures against non-stationary noise.

Case study:                   Pipeline

• The Aframe online pipeline (arXiv:2403.18661) represents a significant step forward.
• Utilizes GPU acceleration for signal preprocessing, model inference, and real-time false alarm rate calculation.

Aframe

S.S. Chaudhary, et al. arXiv:2308.04545

Challenges and Future Directions

• A comprehensive online pipeline should consider various signal types, including BNS and NSBH, and all detectors, including Virgo and KAGRA.
• Beyond false alarm rates, additional real-time information is crucial for a holistic analysis.

Case study:                   Pipeline
 

• The Aframe online pipeline (arXiv:2403.18661) represents a significant step forward.

• Despite its advancements, it primarily focuses on BBH signals, with decreased performance on longer signals like BNS, a point also highlighted in MLGWSC1(2209.11146).

Aframe

Beyond Speed: Generalization and Discovery in GW Detection

• Our primary goal is not speed but the model's ability to generalize and discover new GW signals, including those beyond the reach of matched filtering techniques and General Relativity (GR).
• Leveraging our experience in signal modeling (MFCNN) and noise modeling (WaveFormer), we are gradually building an offline pipeline capable of searching for signals in complete GW observation data and calculating FARs.

Real-time GW searches for GW150914

He Wang, et al. PRD 101, 10 (2020): 104003

He Wang, et al. MLST. 5, 1 (2024): 015046.

Challenges in Model Interpretability

• The black-box nature of AI models poses significant challenges in interpretability, making it difficult to compare AI-generated detection statistics with those derived from matched filtering chi-square distributions.

• Despite being able to identify potential gravitational wave signals, convincing the scientific community of the pipeline's validity and the statistical significance of new discoveries remains a hurdle.

He Wang, et al. MLST. 5, 1 (2024): 015046.

Exploring Beyond General Relativity
 

• Much of the discussion on model generalization has been within the GR framework. Our collaboration with 东北大学 on beyond General Relativity (bGR) aims to demonstrate AI's potential advantages in detecting signals that surpass GR's limitations.

Harsh Narola, et al. “Beyond General Relativity: Designing a Template-Based Search for Exotic Gravitational Wave Signals.” PRD 107, 2 (2023): 024017.

Yu-Xin Wang​, et al. "Draft in Progress"

DINGO: A Leap Forward

• DINGO and related works represent the cutting edge in this field within the LIGO frequency band.
• Tested on 42 BBH events from GWTC-3
• Being deployed for O4, with the potential to become a new gravitational wave signal search pipeline
• Capable of calculating evidence
• Processing time: (using 64 CPU cores)
  • less than 1 hour with IMRPhenomXPHM,
  • approximately 10 hours with SEOBNRv4PHM
• ​Evidence for eccentricity in the population of BBH observed by LVK. (LIGO-G2400750, 2024 Mar)

• 進撃のnflow model in GW inference area.

  • 2002.07656: 5D toy model [1] (PRD)

  • 2008.03312: 15D binary black hole inference [1] (MLST)

  • 2106.12594: Amortized inference and group-equivariant neural posterior estimation [2] (PRL)

  • 2111.13139: Group-equivariant neural posterior estimation [2]

  • 2210.05686: Importance sampling [2] (PRL)

  • 2211.08801: Noise forecasting [2] (PRD)

[1]. https://github.com/stephengreen/lfi-gw  (published @2020)

[2]. https://github.com/dingo-gw/dingo   (published @2023.03)

• Bayesian inference, the Holy Grail of gravitational-wave data analysis,
  enables astrophysical interpretation and scientific discoveries.

Simulation-Based Inference (SBI)

• SBI $\Rightarrow$ Fast and precise parameter estimation.
• SBI $\Rightarrow$ Test of General Relativity (TGR) / Cosmology / PTA ...

PRL 127, 24 (2021) 241103.

PRL 130, 17 (2023) 171403.

Real-time gravitational wave science with neural posterior estimation

Sampling with prior knowledge for high-dimensional gravitational wave data analysis

He Wang, et al. Big Data Min. Anal. (2021)

PRD 108, 4 (2023): 044029.

Appreciating the Ringdown Overtone Test

• A notable work involves ringdown overtone testing, which, acknowledging the difficulty in achieving DINGO-like precision for complex waveforms, leverages the speed advantage of AI.
• By simulating the signal and $10^3$ realizations of LIGO noise for each pixel, it accomplishes what is impossible for MCMC methods, prioritizing speed over precision in a strategic trade-off.

Exploring Stochastic Gravitational Wave Background with AI

• Utilizing AI for parameter estimation in the stochastic gravitational wave background (SGWB) presents a fascinating blend of rich theoretical content and the potential for optimizing current data processing methods.
• While still preliminary and ongoing, our work shows promising results for high SNR SGWB scenarios, where AI-based posterior probabilities are notably more precise and narrower compared to traditional cross-correlation methods used in PyGWB.

Exploring Stochastic Gravitational Wave Background with AI

• Performance saturation is observed between SNR levels of $10^{-6}$ to $10^{-7}$, indicating a plateau in model effectiveness in low SNR conditions.
• Unlike PyGWB, which can accumulate cross-correlation data from SGWB to further constrain the power spectrum, AI model outputs do not readily provide statistically meaningful information for aggregation. Multiplying posterior probabilities from multiple segments leads to ambiguous, and potentially biased, results due to the lack of statistically significant fluctuations across different posterior distributions.

Abbott R, et al. PRD 104, 2 (2021): 022004.

For further reference or to cite the work presented today,
please cite this silde: https://slides.com/iphysresearch/2024mar_bnuz

• Exploring the importance of understanding how AI models make predictions in scientific research.
  • The critical role of generative models (生成模型是关键)
  • Quantifying uncertainty: a key aspect (不确定性量化问题)
  • Fostering controllable and reliable models (模型的可控可信问题)

"A corgi running on the street"

A picture is worth a thousand words.

A fraction of a thousand words.

Credit: 李宏毅

"A running dog"

• The most common and direct approach, from Artificial Intelligence Generated Content (AIGC) to GW statistical inference: pixel point $\Rightarrow$ inferred parameter.