2023年6月27日, 15:00-15:30

量子宇宙理论物理研究中心研讨会

引力波探测与AI技术:揭示宇宙奥秘的新手段

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)

Gravitational Wave Detection and AI Technology:
New Methods for Unveiling the Mysteries of the Universe

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

AI for Gravitational Wave

  • 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

AI for Gravitational Wave: Searches

Matched-filtering Convolutional Neural Network (MFCNN)

PRD 101, 10 (2020) 104003.

GW150914 的实时信号搜寻

  • The majority of machine learning algorithms used for testing are highly sensitive to non-Gaussian real noise backgrounds, resulting in high false positive rates.

PRD 107, 2 (2023): 023021.

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

AI for Gravitational Wave: Searches

  • Expanding the dimension of the output is to call more information to make decisions in improving AI models.

  • Ensemble learning leverages statistical approaches to utilize more information for making informed decisions by combining multiple models.

PRD 105, 8 (2022) 083013

PRD 107, 6 (2023) 063029

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

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

AI for Gravitational Wave: Parameter Estimation

  • A complete 15-dimensional posterior probability distribution, taking about 1 s (<< \(10^4\) s).

Nature Physics 18, 1 (2022) 112–17

An example: Posterior probability distribution of the complete 15-dimensional parameters

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

AI for Gravitational Wave: Parameter Estimation

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

  • Prior Sampling: 50,000 Posterior samples in approximately 8 Seconds.

Big Data Mining and Analytics 5, 1 (2021) 53–63.

  • DINGO (Deep INference for Gravitational wave Observations)
    • 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

PRL 127, 24 (2021) 241103.

PRL 130, 17 (2023) 171403.

AI for Gravitational Wave: 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

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

AI for Gravitational Wave: Data Quality Improvement

BEFORE

AFTER

arXiv:2212.14283, DOI: 10.21203/rs.3.rs-2452860/v1

  • 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

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

AI for Gravitational Wave: 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

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

Bacon P. et al.  arXiv: 2205.13513

AI for Gravitational Wave: 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

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

AI for Gravitational Wave: 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

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

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

  • 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

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! 🙏')

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