He Wang PRO
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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
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Thriving Advancements in AI for GW Astronomy
- GW Searches
- Parameter Estimation
- Noise Characterization
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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.
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Gravitational waves (GW) are a strong field effect in the GR.
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2015: the first experimental detection of GW from the merger of two black holes was achieved.
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2017: the first multi-messenger detection of a BNS signal was achieved, marking the beginning of multi-messenger astronomy.
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2017: the Nobel Prize in Physics was awarded for the detection of GW.
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As of now: more than 90 gravitational wave events have been discovered.
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O4, which began on May 24th 2023, is currently in progress.
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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:
- good detector technology
- good waveform predictions
- good data analysis methodology and technology
- coincident observations in several independent detectors
- 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
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GW Data characteristics:
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Noise: non-Gaussian and non-stationary
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Signal: A low signal-to-noise ratio (SNR) which is typically about 1/100 of the noise amplitude (-60 dB)
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Data quality improvement
Credit: Marco Cavaglià
LIGO-Virgo data processing
GW searches
Astrophsical interpretation of GW sources
-
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
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
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
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
Noise Suppression: Data Quality Improvement
- Billion-scale transformer-based model (WaveFormer)
- Suppression on realistic noise, and
- Recovery of injections / GW events
arXiv:2212.14283
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
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
-
Gravitational-wave astronomy turns to AI:
- A thriving and highly competitive research field on the international stage.
-
AI as a Revolutionary Pathway for Scientific Discoveries:
- Building noise models using AI as a crucial breakthrough for the future.
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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! 🙏')
Smith, Rory. Nature Physics 18, 1 (2022): 9–11
AI for Gravitational Wave: Parameter Estimation
Likelihood
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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
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)
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
- ...
- Characterization of GW signals involves
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
- ...
- Characterization of GW signals involves
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
- ...
- Characterization of GW signals involves
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
-
Gravitational-wave astronomy turns to AI:
- A thriving and highly competitive research field on the international stage.
-
Is AI just a tool? Certainly not! It's a revolutionary pathway for scientific discoveries:
- Enabling new discoveries and insights into the universe.
-
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
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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
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“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
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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
针对引力波观测数据的智能噪声抑制
By He Wang
针对引力波观测数据的智能噪声抑制
2023 中国物理学会秋季学术会议 | 中国·银川
