He Wang PRO
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Nov. 7, 2023, 17:05-17:20
International Workshop on Intelligent Computing in Astronomy
"Computing Senses Cosmos" (2023) @Zhejiang Lab, Hangzhou, China
Gravitational Wave Detection and AI Technology:
New Methods for Unveiling the Mysteries of the Universe
He Wang (王赫)
hewang@ucas.ac.cn
International Centre for Theoretical Physics Asia-Pacific (ICTP-AP), UCAS
Taiji Laboratory for Gravitational Wave Universe, UCAS
on behalf of LVK Collaboration
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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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Gravitational waves generated by binary black holes system
GW detector
LIGO-VIRGO-KAGRA network
2017 Nobel Prize in Physics
Gravitational Wave Astronomy
Gravitational Wave Astronomy
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Fundamental Physics
- Existence of gravitational waves
- To put constraints on the properties of gravitons
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Astrophysics
- Refine our understanding of stellar evolution
- and the behavior of matter under extreme conditions.
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Cosmology
- The measurement of the Hubble constant
- Dark energy
©Floor Broekgaarden (repo)
GWTC-3
- 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
Technical Challenges: Data Processing for GW
GW Data characteristics
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Noise: non-Gaussian and non-stationary
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Signal:
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(Earth-based) A low signal-to-noise ratio (SNR) which is typically about 1/100 of the noise amplitude (-60 dB).
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(Space-based) A superposition of all GW signals (e.g.: \(10^4\) of GBs, \(10\sim10^2\) of SMBHs, and \(10\sim10^3\) of EMRIs, etc.) received during the mission's observational run.
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Matched filtering techniques (匹配滤波方法)
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In Gaussian and stationary noise environments, the optimal linear algorithm for extracting weak signals
- Works by correlating a known signal model \(h(t)\) (template) with the data.
- Starting with data: \(d(t) = h(t) + n(t)\).
- Defining the matched-filtering SNR \(\rho(t)\):
\(\rho^2(t)\equiv\frac{1}{\langle h|h \rangle}|\langle d|h \rangle(t)|^2 \) , where \(\langle d|h \rangle (t) = 4\int^\infty_0\frac{\tilde{d}(f)\tilde{h}^*(f)}{S_n(f)}e^{2\pi ift}df \) ,
\(\langle h|h \rangle = 4\int^\infty_0\frac{\tilde{h}(f)\tilde{h}^*(f)}{S_n(f)}df \), \(S_n(f)\) is noise power spectral density (one-sided).
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LIGO-VIRGO-KAGRA
LISA / Taiji project
AI for 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.
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PRL, 2018, 120(14): 141103.
PRD, 2018, 97(4): 044039.
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.
Exported: Oct, 2023 (in preparation)
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GW Detection: Discriminative Learning
CL.M., W.W., H.W., et al. PRD (2022)
Ensemble learning
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Leverages statistical approaches to utilize more information for making informed decisions by combining multiple models.
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MLGWSC-1
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The majority of AI algorithms used for testing are highly sensitive to non-Gaussian real noise backgrounds, resulting in high false positive rates.
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(MFCNN group) H.W., et al. PRD (2023)
Expanding the dimension of the output
- is to call more information to make decisions in improving AI models.
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CL.M., W.W., H.W., et al. PRD (2023)
Matched-filtering Convolutional Neural Network (MFCNN)
- GW templates can be utilized as recognizable features for signal detection.
- It is feasible to generalize both matched-filtering and neural networks.
- Linear filters (i.e., matched-filtering) in signal processing can be reformulated as neural layers (i.e., CNNs).
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Real-time GW searches for GW150914
H.W., et al. PRD (2020)
GW Denoising: Discriminative Learning
Large Language Model (LLM)
- Representation learning is conducted using a billion-scale transformer-based model (encoder-only).
- Segments of sampling points are considered as the processing units for each character: tokens \(\Rightarrow\) segments of sampling point.
H.W., et al. arXiv:2212.14283
DOI: 10.21203/rs.3.rs-2452860/v1
TY.Z., R.L., H.W., et al. Commun. Phys. (2023)
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Large Language Model (LLM)
- Noise suppression and recovery of GW events in actual LIGO recordings.
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"One Model to Rule Them All":
- Signal extraction of EMRI / MBHB / GBs / SGWB in the LISA mock challenge (LDC).
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H.W., et al. arXiv:2212.14283
DOI: 10.21203/rs.3.rs-2452860/v1
TY.Z., R.L., H.W., et al. Commun. Phys. (2023)
MBHB
EMRI
GB
GW Denoising: Discriminative Learning
AI Predicting the Universe: Opportunities and Challenges
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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 131, 17 (2023): 171403.
Angular Power Spectrum of Gravitational-Wave Transient Sources as a Probe of the Large-Scale Structure
Angular Power Spectrum of Gravitational-Wave Transient Sources as a Probe of the Large-Scale Structure
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arXiv:2310.12209
Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows
PRD 108, 4 (2023): 044029.
Neural Posterior Estimation with Guaranteed Exact Coverage: The Ringdown of GW150914
arXiv:2310.13405, LIGO-P2300306
Cosmological Inference using Gravitational Waves and Normalising Flows
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
H.W., et al. Big Data Min. Anal. (2021)
AI Predicting the Universe: Opportunities and Challenges
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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)
Text-to-image
AI or Bayes
"A running dog"
- The most common and direct approach, from Artificial Intelligence Generated Content (AIGC) to GW statistical inference: pixel point \(\Rightarrow\) inferred parameter.
AI Predicting the Universe: Opportunities and Challenges
"A running dog"
Text-to-image
"A corgi running on the street"
AI or Bayes
- The most common and direct approach, from Artificial Intelligence Generated Content (AIGC) to GW statistical inference: pixel point \(\Rightarrow\) inferred parameter.
A picture is worth a thousand words.
A fraction of a thousand words.
Credit: 李宏毅
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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)
Key Takeaways
Nature Physics 18, 1 (2022): 9–11
arXiv:2308.05510, accepted by SCPMA.
On-going
- About Data: We are developing a software toolkit called GWToolkit that integrates gravitational wave signal processing and generic asynchronous data pipeline capabilities.
- About models: Let's continue to leverage representation learning and explore the use of GPT-like language models for scientific discovery.
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Insights
- AI is not just a tool; it is a revolutionary pathway for scientific discoveries.
- The future is filled with technical challenges, even when it comes to using AI, including:
- Super high-dimensional parameter inference
- Super high-dimension of GW data strains
- "Gravitational Wave Astrostatistics" has the potential to become a new field of knowledge.
- Improve the interpretability of AI models, as it is essential for enhanced and trustworthy discoveries.
Text
Dynamic training samples in memory.
Key Takeaways
Nature Physics 18, 1 (2022): 9–11
arXiv:2308.05510, accepted by SCPMA.
On-going
- About Data: We are developing a software toolkit called GWToolkit that integrates gravitational wave signal processing and generic asynchronous data pipeline capabilities.
- About models: Let's continue to leverage representation learning and explore the use of GPT-like language models for scientific discovery.
Text
Insights
- AI is not just a tool; it is a revolutionary pathway for scientific discoveries.
- The future is filled with technical challenges, even when it comes to using AI, including:
- Super high-dimensional parameter inference
- Super high-dimension of GW data strains
- "Gravitational Wave Astrostatistics" has the potential to become a new field of knowledge.
- Improve the interpretability of AI models, as it is essential for enhanced and trustworthy discoveries.
Text
Dynamic training samples in memory.
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')Key Takeaways
“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。
Gravitational Wave Detection and AI Technology: New Methods for Unveiling the Mysteries of the Universe
By He Wang
Gravitational Wave Detection and AI Technology: New Methods for Unveiling the Mysteries of the Universe
Nov. 7, 2023, 17:05-17:20. International Workshop on Intelligent Computing in Astronomy "Computing Senses Cosmos" (2023) @Zhejiang Lab, Hangzhou, China
