2023年8月20日, 12:00-12:15

2023 中国物理学会秋季学术会议 | 中国·银川

针对引力波观测数据的智能噪声抑制

王赫

hewang@ucas.ac.cn

中国科学院大学 · 国际理论物理中心（亚太地区）

中国科学院大学 · 引力波宇宙太极实验室（北京/杭州）

Intelligent Noise Suppression for GW Observational Data

Content

  • Thriving Advancements in AI for GW Astronomy
    • GW Searches
    • Parameter Estimation
    • Noise Characterization
  • Challenge in GW Data Processing: Lessons and Future
    • AI: A transformative force driving scientific breakthroughs
  • AI for Science: GW Astronomy
    • Interpretability of AI in science
  • Key Takeaways

  • In 1916, A. Einstein proposed the GR and predicted the existence of GW.

  • Gravitational waves (GW) are a strong field effect in the GR.

    • 2015: the first experimental detection of GW from the merger of two black holes was achieved.

    • 2017: the first multi-messenger detection of a BNS signal was achieved, marking the beginning of multi-messenger astronomy.

    • 2017: the Nobel Prize in Physics was awarded for the detection of GW.

    • As of now: more than 90 gravitational wave events have been discovered.

    • O4, which began on May 24th 2023, is currently in progress.

双星并合系统产生的引力波波源

引力波振幅的测量

地面引力波探测器网络

2017 年诺贝尔物理学奖

Gravitational Wave Astronomy

Gravitational Wave Astronomy

  • Fundamental Physics
    • Existence of gravitational waves
    • To put constraints on the properties of gravitons
  • Astrophysics
    • Refine our understanding of stellar evolution
    • and the behavior of matter under extreme conditions.
  • Cosmology
    • The measurement of the Hubble constant
    • Dark energy
  • Detecting gravitational waves require a mix of FIVE key ingredients:
    1. good detector technology
    2. good waveform predictions
    3. good data analysis methodology and technology
    4. coincident observations in several independent detectors
    5. coincident observations in electromagnetic astronomy

—— Bernard F. Schutz

​​DOI:10.1063/1.1629411 

©Floor Broekgaarden (repo)

GWTC-3

  • AI for Science \(\rightarrow\) AI for GW
  • Artificial Intelligence (AI) has great potential to revolutionize gravitational wave astronomy by improving data analysis, modeling, and detector development.

AI for Gravitational Wave

GW Data Challenge

  • GW Data characteristics:

    • ​Noise: non-Gaussian and non-stationary

    • Signal: A low signal-to-noise ratio (SNR) which is typically about 1/100 of the noise amplitude (-60 dB)

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

CQG. 37 (2020) 055002

  • GW Data characteristics:

    • ​Noise: non-Gaussian and non-stationary

    • Signal: A low signal-to-noise ratio (SNR) which is typically about 1/100 of the noise amplitude (-60 dB)

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

CQG. 37 (2020) 055002

GW Data Challenge

Noise Suppression: Data Quality Improvement

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

BEFORE

AFTER

Noise Suppression: Data Quality Improvement

BEFORE

AFTER

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

Noise Suppression: Data Quality Improvement

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

Bacon P. et al.  arXiv: 2205.13513

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

Noise Suppression: Data Quality Improvement

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

Bacon P. et al.  arXiv: 2205.13513

Murali C & Lumley D. arXiv: 2210.01718

Wei W and Huerta E A. PLB 2020

Chatterjee C, Wen L, et al. PRD 2021

Noise Suppression: Data Quality Improvement

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

Noise Suppression: Adaptive Approach for Multimodal Source

  • Noise suppression on multimodal sources for space-based scenarios.
    • "One Model to Rule Them All".

 T.Z, R.L, H.W, et al. "Space-based gravitational wave signal detection and extraction with deep neural network" Communications Physics, (2023)

Credit: ESA, K. Holley-Bockelmann

Key Takeaways

  1. Gravitational-wave astronomy turns to AI:
    • A thriving and highly competitive research field on the international stage.
  2. AI as a Revolutionary Pathway for Scientific Discoveries:
    • Building noise models using AI as a crucial breakthrough for the future.
  3. Future Research Directions:
    • Achieving rapid and adaptive noise modeling on complex empirical GW data.
for _ in range(num_of_audiences):
    print('Thank you for your attention! 🙏')

This slide: https://slides.com/iphysresearch/2023aug_denoising

Smith, Rory. Nature Physics 18, 1 (2022): 9–11

AI for Gravitational Wave: Parameter Estimation

Likelihood

  • Traditional parameter estimation (PE) techniques rely on Bayesian analysis methods (posteriors + evidence)

  • Computing the full 15-dimensional posterior distribution estimate is very time-consuming:
    • Calculating likelihood function
    • Template generation time-consuming
  • Machine learning algorithms are expected to speed up! 

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

CQG. 37 (2020) 055002

Challenge in GW Data Analysis: Lessons and Future

  • Space-based GW Observatories (LISA / Taiji / TianQin)

LIGO-G2300554

  • Next-Generation Ground-Based GW Observatory (3G: CE / ET)

PLB 841 (2023) 137904.

arXiv: 2207.07414

PRL 130, 17 (2023) 171402.

  • Posteriors from AI are generally broader but include the injected value within the 90% confidence interval.
  • AI serves as a valuable tool in gravitational wave astronomy:
    (Big data & Computational Complexity)
    • Enhancing data analysis,
    • Noise reduction, and
    • Parameter estimation.
    • It streamlines the research process and allows scientists to focus on the most relevant information.
  • Beyond a Tool: AI transcends its role as a mere tool by enabling scientific discovery in GW astronomy.
    • Characterization of GW signals involves
      • Exploring beyond the scope of GR ,
      • Enabling real-time inference
    • "Curse of Dimensionality" in inference
      • Overlapping signal  (In progress)
      • Hierarchical Bayesian Analysis (In progress)
    • Test of GR
      • Tighter parameter constraints of variance
      • Guaranteed exact coverage
    • ...

Challenge in GW Data Analysis: Lessons and Future

GW170817
GW190412
GW190814

Bayes factor (MCMC)

PRD 101, 10 (2020) 104003.

(In preparation)

Challenge in GW Data Analysis: Lessons and Future

arXiv: 2211.01304

  • AI serves as a valuable tool in gravitational wave astronomy:
    (Big data & Computational Complexity)
    • Enhancing data analysis,
    • Noise reduction, and
    • Parameter estimation.
    • It streamlines the research process and allows scientists to focus on the most relevant information.
  • Beyond a Tool: AI transcends its role as a mere tool by enabling scientific discovery in GW astronomy.
    • Characterization of GW signals involves
      • Exploring beyond the scope of GR , 
      • Enabling real-time inference
    • "Curse of Dimensionality" in inference
      • Overlapping signal  (In progress)
      • Hierarchical Bayesian Analysis (In progress)
    • Test of GR
      • Tighter parameter constraints of variance
      • Guaranteed exact coverage
    • ...

Combining inferences from multiple sources

PRD 99, 124044 (2019)

arXiv:2305.18528

Challenge in GW Data Analysis: Lessons and Future

  • AI serves as a valuable tool in gravitational wave astronomy:
    (Big data & Computational Complexity)
    • Enhancing data analysis,
    • Noise reduction, and
    • Parameter estimation.
    • It streamlines the research process and allows scientists to focus on the most relevant information.
  • Beyond a Tool: AI transcends its role as a mere tool by enabling scientific discovery in GW astronomy.
    • Characterization of GW signals involves
      • Exploring beyond the scope of GR , 
      • Enabling real-time inference
    • "Curse of Dimensionality" in inference
      • Overlapping signal  (In progress)
      • Hierarchical Bayesian Analysis (In progress)
    • Test of GR
      • Tighter parameter constraints of variance
      • Guaranteed exact coverage
    • ...

ICML2023

AI for Science: GW Astronomy

  • 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)

Bayes

AI

Credit: 李宏毅

Text-to-image

AI for Science: GW Astronomy

  • 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)

Bayes

AI

Credit: 李宏毅

Text-to-image

Key Takeaways

  1. Gravitational-wave astronomy turns to AI:
    • A thriving and highly competitive research field on the international stage.
  2. Is AI just a tool? Certainly not! It's a revolutionary pathway for scientific discoveries:
    • Enabling new discoveries and insights into the universe.
  3. Addressing Challenges in AI Interpretability:
    • Essential for enhanced discoveries
for _ in range(num_of_audiences):
    print('Thank you for your attention! 🙏')

This slide: https://slides.com/iphysresearch/zju_20230626

Smith, Rory. Nature Physics 18, 1 (2022): 9–11

Outlook

  • 值得关注的 AI 技术：
    • Large Language Model (LLM)
    • AI generated content (AIGC)

WaveFormer

Transformer: 750x / 2yrs

AI for Science

  • 2016年，AlphaGo 第一版发表在了 Nature 杂志上

  • 2021年，AI预测蛋白质结构登上 Science、Nature 年度技术突破，潜力无穷

  • 2022年，DeepMind团队通过游戏训练AI发现矩阵乘法算法问题​

  • 《达摩院2022十大科技趋势》将 AI for Science 列为重要趋势

    • “人工智能成为科学家的新生产工具，催生科研新范式”

  • AI for Science：为科学带来了模型与数据双驱动的新的研究范式

    • AI + 数学、AI + 化学、AI + 医药、AI + 物理、AI + 天文 ...

AlphaGo 围棋机器人

AlphaTensor 发现矩阵算法

AlphaFold 蛋白质结构预测