2023年9月25日, 15:43-16:03

第二届“机器学习在天文学中的应用研讨会 (2023) | 湖北 · 宜昌

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）

on behalf of LIGO-VIRGO-KAGRA Collaboration

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

• Thriving Advancements in AI for GW Astronomy
  • GW Searches
  • Parameter Estimation
  • Noise Characterization
• Space-based Gravitational Wave Data Analysis
• AI for Science: GW Astronomy
• Key Takeaways

Taiji

• 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

• 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

©Floor Broekgaarden (repo)

• 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.

• 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

Matched-filtering Convolutional Neural Network (MFCNN)

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

LIGO-Virgo data processing

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

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

Bayesian statistics

• 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

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

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

• Capable of calculating evidence
• Processing time: (using 64 CPU cores)
  • less than 1 hour with IMRPhenomXPHM,
  • approximately 10 hours with SEOBNRv4PHM

• 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

CQG. 37 (2020) 055002

• 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

LIGO-Virgo data processing

• 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

LIGO-Virgo data processing

• 空间引力波探测主要有以下 4 类波源：

  • 恒星级质量的致密双星 (黑洞、中子星、白矮星以及它们的两两组合) 的旋近

  • 双白矮星的并合、​超大质量双黑洞的并合

  • 极端质量比旋进 (通常是一个恒星级致密天体绕着一个超大质量黑洞的旋进）

  • 宇宙中可能存在的中等质量双黑洞以及前面这些源的信号叠加形成的引力波背景

LIGO-G2300554

• Global-fit approach + AI-powered approach
  • Searches: Discriminative model
  • Inference: Generative model

PLB 841 (2023) 137904.

Communications Physics, 2023, 6(1): 212.

arXiv:2308.05510

Universe, 2023, 9(9): 407.

Nature Astronomy, 2022, 6(12): 1356-1363.

Nature Astronomy, 2022, 6(12): 1334-1338.

Schematic view of the global fit approach.

• 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
  • ...

PRD 101, 10 (2020) 104003.

(In preparation)

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
  • ...

arXiv:2305.18528

• 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

• Using Large Language Models (LLMs) for scientific discovery.
• The need for a scientific infrastructure for AI in Science.

• 以国家天文科学数据中心在线服务平台为基础，开发适用于引力波天文学研究的引力波探测开源数据门户 (2023-2025)。

• 开发一套集成引力波数据处理与通用异步计算功能的软件工具包：GWToolkit

• 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

LIGO-P2300306

• 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

LIGO-P2300306

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 for GW astronomy: essential for enhanced discoveries
   • LLM
   • Scientific infrastructure
   • Interpretability

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

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

“国际理论物理中心(亚太地区)” 经联合国教科文组织第38届大会审议通过。由中国科学院、基金委和国际理论物理中心共同建设，是进行基础科学前沿与相关交叉科学领域高水平科研、教育和培训的非营利性组织，是联合国教科文组织基础科学方面的在国内的第一个二类中心。

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

WaveFormer

Transformer: 750x / 2yrs

• 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