He Wang PRO
Knowledge increases by sharing but not by saving.
12月28日 @武汉大学
王赫
hewang@ucas.ac.cn
International Centre for Theoretical Physics Asia-Pacific (ICTP-AP), UCAS
Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), UCAS
On behalf of the LIGO-VIRGO-KAGRA collaborations
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.
Gravitational waves generated by binary black holes system
GW detector
LIGO-VIRGO-KAGRA network
2017 Nobel Prize in Physics
引力波探测打开了探索宇宙的新窗口
不同波源,频率跨越 20 个数量级,不同探测器
多信使天文学
The first GW event of GW150914
—— Bernard F. Schutz
DOI: 10.1063/1.1629411
GWTC-3
—— Bernard F. Schutz
DOI: 10.1063/1.1629411
GWTC-3
©Floor Broekgaarden (repo)
GW Data characteristics
Noise: non-Gaussian and non-stationary
Signal:
(Earth-based) A low signal-to-noise ratio (SNR) which is typically about 1/100 of the noise amplitude (-60 dB).
(Space-based) A superposition of all GW signals (e.g.: 104 of GBs, 10∼102 of SMBHs, and 10∼103 of EMRIs, etc.) received during the mission's observational run.
Matched filtering techniques (匹配滤波方法)
In Gaussian and stationary noise environments, the optimal linear algorithm for extracting weak signals
LIGO-VIRGO-KAGRA
LISA / Taiji project
Frequentist hypothesis testing and likelihood princple:
make some assumptions about signal and noise hypothesis
write down the likelihood function for a signal in noise
find the parameters that maximise it
define a corresponding detection statistic
→ recover the MF2016年,AlphaGo 第一版发表在了 Nature 杂志上
2021年,AIphaFold 预测蛋白质结构登上 Science、Nature 年度技术突破
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”
2023年,DeepMind发布AI工具GNoME (Nature),成功预测220万种晶体结构
2023年3月,为贯彻落实国家《新一代人工智能发展规划》,科技部会同自然科学基金委启动“人工智能驱动的科学研究”(AI for Science)专项部署工作,布局“人工智能驱动的科学研究”前沿科技研发体系。
AlphaGo 围棋机器人
AlphaTensor 发现矩阵算法
AlphaFold 蛋白质结构预测
验证数学猜想
AlphaGo 围棋机器人
AlphaTensor 发现矩阵算法
AlphaFold 蛋白质结构预测
验证数学猜想
2016年,AlphaGo 第一版发表在了 Nature 杂志上
2021年,AIphaFold 预测蛋白质结构登上 Science、Nature 年度技术突破
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”
2023年,DeepMind发布AI工具GNoME (Nature),成功预测220万种晶体结构
2023年3月,为贯彻落实国家《新一代人工智能发展规划》,科技部会同自然科学基金委启动“人工智能驱动的科学研究”(AI for Science)专项部署工作,布局“人工智能驱动的科学研究”前沿科技研发体系。
Pioneering works utilizing CNN
AI for Science → AI for GW Astronomy
Exported: Oct, 2023 (in preparation)
PRL, 2018, 120(14): 141103.
PRD, 2018, 97(4): 044039.
Matched-filtering Convolutional Neural Network (MFCNN)
MLGWSC-1
The majority of AI algorithms used for testing are highly sensitive to non-Gaussian real noise backgrounds, resulting in high false positive rates.
(MFCNN group) H.W., et al. PRD (2023)
CL.M., W.W., H.W., et al. PRD (2022)
Ensemble learning
Leverages statistical approaches to utilize more information for making informed decisions by combining multiple models.
Real-time GW searches for GW150914
H.W., et al. PRD (2020)
Expanding the dimension of the output
CL.M., W.W., H.W., et al. PRD (2023)
OURs
LVK. PRD (2016). arXiv:1602.03839
GW151226
GW151012
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
Exploring Beyond General Relativity
Yu-Xin Wang, et al. "Draft in Progress"
B. P. Abbott et al. (LIGO-Virgo), PRD 100, 104036 (2019).
Credit: LIGO Magazine.
Traditional parameter estimation (PE) techniques rely on Bayesian analysis methods (posteriors + evidence)
Bayesian statistics
Data quality improvement
Credit: Marco Cavaglià
LIGO-Virgo data processing
GW searches
Astrophsical interpretation of GW sources
PRL 127, 24 (2021) 241103.
PRL 130, 17 (2023) 171403.
Nature Physics 18, 1 (2022) 112–17
Big Data Mining and Analytics 5, 1 (2021) 53–63.
A diagram of prior sampling between feature space and physical parameter space
(Based on 1912.02762)
【【机器学习】白板推导系列(三十三) ~ 流模型(Flow based Model)】
The main idea of flow-based modeling is to express y∈RD as a transformation T of a real vector z∈RD sampled from pz(z):
Note: The invertible and differentiable transformation T and the base distribution pz(z) can have parameters {ϕ,ψ} of their own, i.e. Tϕ and pz,ψ(z).
Change of Variables:
Equivalently,
The Jacobia JT(u) is the D×D matrix of all partial derivatives of T given by:
base density
target density
(Based on 1912.02762)
base density
target density
Rational Quadratic Neural Spline Flows
(RQ-NSF)
Train
nflow
归一化流模型示意图
Test
nflow
Train
nflow
Simulation-Based Inference (SBI)
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
He Wang, et al. Big Data Min. Anal. (2021)
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
Normalizing Flows as an Avenue to Studying Overlapping Gravitational Wave Signals
arXiv:2310.12209
Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows
arXiv:2404.14286
Exact coverage first!
Paradigm
New
discovery
first!
PRD 108, 4 (2023): 044029.
Appreciating the Ringdown Overtone Test of GW150914
arXiv:2404.14286
進撃のnflow model in GW inference area.
2002.07656: 5D toy model [1] (PRD)
2008.03312: 15D binary black hole inference [1] (MLST)
2106.12594: Amortized inference and group-equivariant neural posterior estimation [2] (PRL)
2111.13139: Group-equivariant neural posterior estimation [2]
2210.05686: Importance sampling [2] (PRL)
2211.08801: Noise forecasting [2] (PRD)
2305.17161: FMPE
2404.14286: eccentricity of BBHs
https://github.com/dingo-gw/dingo (2023.03)
Gravitational waves and sources:
Credit: ESA, K. Holley-Bockelmann
(Sec.8.3.1 The Red Book)
The analysis of scientific data from space-based GW detection differs significantly from ground-based detection:
空间引力波探测科学数据处理的挑战与人工智能技术的应用
王赫, 杜明辉, 徐鹏, 周宇峰
2024年, 第54卷, 第7期, 270403
Analyses cannot treat sources independently and sequentially work through a list of candidate detections.
(Sec.8.6 The Red Book)
Global Fit
Technical challenges:
M. Du, B. Liang, HW, P. Xu, Z. Luo, Y. Wu. SCPMA 67, 230412 (2024).
Global vs. Individual Analysis: While global-fit techniques effectively manage the dense overlapping of signals in space-based GW data, individual pipelines are crucial for detecting unique events.
Role of Individual Pipelines: These pipelines act as a pre-processing step, focusing on particular types of sources and diving deeper into the data. They refine the analysis by working on the latest best-fit residuals from the global fit.
Case Study - MBHB Mergers: Mergers of MBHBs often exhibit high SNR between 102 to 103, appearing as distinct peaks in data time series.
Data curation
Model: frequency domain; PhenomD; TDI-A/E response
Input: 1 day length; 15Hz; shape=(2, 3, 2877)
Noise: Gaussian stationary from the noise PSD (for training/test) + GB confusion noise (for test)
Project: Taiji program
M. Du, B. Liang, HW, P. Xu, Z. Luo, Y. Wu. SCPMA 67, 230412 (2024).
The top section of the illustration shows the solar system barycenter (SSB) and Taiji frames, with two black dashed arrows symbolizing not two separate GW signals, but rather indicating how the sky location and arrival time of the same GW signal take different values in these two frames.
The “positive” problem translates the SSB-frame parameters to their Taiji-frame counterparts via a time-dependent mapping f1, then to the TDI outputs through a time-independent mapping f2, and an exponential term.
TDI-A
These steps can be schematically summarized as:
where TαA,E(f) is often referred to as the transfer function.
M. Du, B. Liang, HW, P. Xu, Z. Luo, Y. Wu. SCPMA 67, 230412 (2024).
Consequently, even if the network has only learned the time-dependent relationship between θS and the TDI response at a specific tref (the 30th day in our case), with the aid of coordinate transformation, it has essentially learned the time-invariant mapping f2, and can be then generalized to make parameter estimation at any other reference time.
It is worth noting that our method relies on analytical orbits and
the time-independence of the coordinate transformation f2.
The top section of the illustration shows the solar system barycenter (SSB) and Taiji frames, with two black dashed arrows symbolizing not two separate GW signals, but rather indicating how the sky location and arrival time of the same GW signal take different values in these two frames.
The “positive” problem translates the SSB-frame parameters to their Taiji-frame counterparts via a time-dependent mapping f1, then to the TDI outputs through a time-independent mapping f2, and an exponential term.
1 year length
can infer at any other reference time
trained on the 30th day only
M. Du, B. Liang, HW*, P. Xu, Z. Luo, Y. Wu*. SCPMA 67, 230412 (2024).
Methodology: Utilization of the Kolmogorov-Smirnov (KS) test to compare one-dimensional distributions generated by our algorithms, ensuring the accuracy of parameter estimation.
Empirical Validation: Conducted extensive testing on simulated signals, injecting 1000 waveforms from the prior with added confusion noise and varying reference times between 1 and 365 days.
Results: The tests assessed the frequency at which true parameters fell within certain confidence levels, confirming that our credible intervals are well-calibrated and reflect true confidence in the signal parameters.
Computational performance
10000 posterior samples in 2.7 sec
M. Du, B. Liang, HW, P. Xu, Z. Luo, Y. Wu. SCPMA 67, 230412 (2024).
Overview of Findings: Nested sampling results indicate minimal expected multimodality in ecliptic coordinates. However, distinct peaks identified in the time of coalescence (tc), labeled as NF-1 (dominant) and NF-2 (subdominant), highlight unique multimodal behavior.
(NF = Normalizing Flow model)
M. Du, B. Liang, HW, P. Xu, Z. Luo, Y. Wu. SCPMA 67, 230412 (2024).
Bo Liang, Minghui Du*, He Wang*, et al. “Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows.” arXiv, October 7, 2024. http://arxiv.org/abs/2407.07125. (MLST accepted)
Data Analysis for LISA/Taiji/Tianqin
Credit: Natalia Korsakova
(LISA Symposium 2024 Dubin)
Galactic Compact Binaries
New catalog with parameters can be found here: https://gitlab.in2p3.fr/LISA/lisa-verification-binaries
credit: Karnesis et al, arXiv:2303.02164v2
The analysis of the best currently known LISA binaries, even making maximal use of the available information about the sources, is susceptible to ambiguity or biases when not simultaneously fitting to the rest of the galactic population.
(copied from Littenberg et al. 2404.03046)
credit: Kupfer et al, arXiv:2302.12719
credit: Kupfer et al, arXiv:2302.12719
Massive Black Hole Binaries
Credit: Geraint Pratten (LISA Symposium 2024 Dubin)
Credit: Sylvain Marsat
(LISA Symposium 2024 Dubin)
Massive Black Hole Binaries
The addition of GBs biases the parameter recovery of masses and spins away from the injected values, reinforcing the need for a global fit pipeline which will simultaneously fit the parameters of the GB signals before estimating the parameters of MBHBs. (Copied from Weaving, et al. CQG, 2023, 41(2): 025006.)
Pipeline | Targets | Programing Language (sampling method) | Comments |
---|---|---|---|
GLASS (Littenberg&Cornish 2023) |
Noise, UCB, VGB, MBHB |
C / Python (TPMCMC / RJMCMC) | noise_mcmc+gb_mcmc+vb_mcmc+global_fit |
Strub et al. | UCB/MBHB | ? (EA/MCMC) | Zenodo / GPU-based |
Gee-Moo | Noise, UCB, VGB, MBHB |
? (TPMCMC / RJMCMC) | Unavailable (from APC) |
PyCBC-INFERENCE | MBHB | Python (?) | Unavailable |
Eryn | UCB | Python (TPMCMC / RJMCMC) | Mini code for UCB case |
Bilby in Space / tBilby | MBHB / ? | ? / Python? (RJMCMC) | Unavailable |
Zhang et al. (LZU) | UCB | ? (PSO) | MLE |
Balrog | MBHB | ? |
(Sec.8.6 Red Book)
Global Fit
Technical challenges:
GLASS (Littenberg&Cornish 2023)
Strub et al. (2024,2023)
GLASS (Littenberg&Cornish 2023)
Strub et al. (2024,2023)
GLASS (Littenberg&Cornish 2023)
Strub et al. (2024,2023)
GLASS (Littenberg&Cornish 2023)
Strub et al. (2024,2023)
GLASS (Littenberg&Cornish 2023)
Strub et al. (2024,2023)
空间引力波探测科学数据处理的挑战与人工智能技术的应用
王赫, 杜明辉, 徐鹏, 周宇峰
2024年, 第54卷, 第7期, 270403
GLASS (Littenberg&Cornish 2023)
中国科学院计算机网络信息中心“东方”超级计算系统 (全国产CPU/GPGPU)
Use Ray for distributed computing on both CPU anD GPUs
Neural density estimation
Neural density estimation
Ref:
nflow
Neural density estimation
Ref:
nflow
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')
This silde: https://slides.com/iphysresearch/2024Oct_WHU
By He Wang
2024.10.28 @武汉大学