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
摘要:
引力波的探测为基础物理学和宇宙学等研究带来了革命性的进展,但由此产生的数据通常受到噪声等干扰,分析难度极大。近年来,人工智能(AI)技术,尤其是深度学习在引力波探测领域展现了巨大的潜力。
本次报告将探讨AI在引力波信号处理中的应用,
重点介绍其在引力波去噪、多频段数据分析及参数反演中的重要作用。
同时,我们将讨论全局搜索方法在复杂引力波信号中的应用,
尤其是如何有效应对引力波信号的高维度与多模态数据。
通过深入讨论AI在引力波探测中的机遇与挑战,我们展望其在宇宙学与天体物理研究中的未来应用潜力。
AI for GW
AI for PE
Space-bone GW
global-fit
Taiji
Tianqin
https://twitter.com/chipro/status/1768388213008445837?s=46&t=JmDXWgIucgr_FlsBFTvuRQ
DINGO+SEOBNRv4EHM找了3个ebbh
Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA
https://dcc.ligo.org/LIGO-G2400750
BEFORE
AFTER
LIGO-G2300554
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.: \(10^4\) of GBs, \(10\sim10^2\) of SMBHs, and \(10\sim10^3\) 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
\(\rightarrow\) 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 \(\rightarrow\) AI for GW Astronomy
Exported: Oct, 2023 (in preparation)
PRL, 2018, 120(14): 141103.
PRD, 2018, 97(4): 044039.
Introduction to Speed and Efficiency
The Need for Integration (an AI pipeline!)
Case study: Pipeline
Aframe
S.S. Chaudhary, et al. arXiv:2308.04545
Challenges and Future Directions
Case study: Pipeline
Aframe
OpenLVEM, June 08, 2023. Low Latency UPDATE.
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)
Beyond Speed: Generalization and Discovery in GW Detection
Real-time GW searches for GW150914
He Wang, et al. PRD 101, 10 (2020): 104003
He Wang, et al. MLST. 5, 1 (2024): 015046.
Beyond Speed: Generalization and Discovery in GW Detection
He Wang, et al. PRD 101, 10 (2020): 104003
He Wang, et al. MLST. 5, 1 (2024): 015046.
Real-time GW searches for GW150914
数据增益 -> GWToolkit (缺一个好看的高吞吐性能图和loss/acc性能图)+ Data Protal (缺一个可视化和性能表现CKAN?)
Pipeline -> MFCNN + WaveFormer(缺雪藏event的corner图); bGR(缺结果图)
信号探测(缺各种文章中的探测统计量图像)-> 引出新的理论缺失: machine learning GW statistics
Beyond Speed: Generalization and Discovery in GW Detection
Feature extraction
Convolutional Neural Network (ConvNet or CNN)
Classification
GW150914
GW151226
GW151012
>> Is it matched-filtering ?
>> Wait, It can be matched-filtering!
Beyond Speed: Generalization and Discovery in GW Detection
He Wang, et al. PRD 101, 10 (2020): 104003
GW150914
GW151226
GW151012
MFCNN
MFCNN
MFCNN
Matched-filtering (cross-correlation with the templates) can be regarded as a convolutional layer with a set of predefined kernels.
Real-time GW searches for GW150914
Beyond Speed: Generalization and Discovery in GW Detection
He Wang, et al. PRD 101, 10 (2020): 104003
Frequency domain
Beyond Speed: Generalization and Discovery in GW Detection
He Wang, et al. PRD 101, 10 (2020): 104003
Frequency domain
Time domain
(normalizing)
(matched-filtering)
\(S_n(|f|)\) is the one-sided average PSD of \(d(t)\)
(whitening)
where
Beyond Speed: Generalization and Discovery in GW Detection
He Wang, et al. PRD 101, 10 (2020): 104003
Frequency domain
Time domain
(normalizing)
(matched-filtering)
\(S_n(|f|)\) is the one-sided average PSD of \(d(t)\)
(whitening)
where
Deep Learning Framework
FYI: \(N_\ast = \lfloor(N-K+2P)/S\rfloor+1\)
(A schematic illustration for a unit of convolution layer)
Beyond Speed: Generalization and Discovery in GW Detection
He Wang, et al. PRD 101, 10 (2020): 104003
Frequency domain
Time domain
(normalizing)
(matched-filtering)
\(S_n(|f|)\) is the one-sided average PSD of \(d(t)\)
(whitening)
where
Deep Learning Framework
Beyond Speed: Generalization and Discovery in GW Detection
He Wang, et al. PRD 101, 10 (2020): 104003
Time domain
(normalizing)
(matched-filtering)
\(S_n(|f|)\) is the one-sided average PSD of \(d(t)\)
(whitening)
where
Deep Learning Framework
modulo-N circular convolution
Beyond Speed: Generalization and Discovery in GW Detection
He Wang, et al. PRD 101, 10 (2020): 104003
import mxnet as mx
from mxnet import nd, gluon
from loguru import logger
def MFCNN(fs, T, C, ctx, template_block, margin, learning_rate=0.003):
logger.success('Loading MFCNN network!')
net = gluon.nn.Sequential()
with net.name_scope():
net.add(MatchedFilteringLayer(mod=fs*T, fs=fs,
template_H1=template_block[:,:1],
template_L1=template_block[:,-1:]))
net.add(CutHybridLayer(margin = margin))
net.add(Conv2D(channels=16, kernel_size=(1, 3), activation='relu'))
net.add(MaxPool2D(pool_size=(1, 4), strides=2))
net.add(Conv2D(channels=32, kernel_size=(1, 3), activation='relu'))
net.add(MaxPool2D(pool_size=(1, 4), strides=2))
net.add(Flatten())
net.add(Dense(32))
net.add(Activation('relu'))
net.add(Dense(2))
# Initialize parameters of all layers
net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx, force_reinit=True)
return net
The available codes: https://gist.github.com/iphysresearch/a00009c1eede565090dbd29b18ae982c
1 sec duration
35 templates used
1400Ripples Air Compressor Blip
Extremely Loud Helix Koi Fish
Various types of Glitch
The improvement of data quality is a very complex issue, with data from over 20,000 sensor channels determining the quality of the gravitational wave science data channel.
Reducing non-Gaussian short-duration pulse interference (Glitches) in gravitational wave data will help reduce the false alarm rate of gravitational wave signals.
Removing Glitches from gravitational wave detection data is a multi-classification problem.
Ormiston R, et al. PRR, 2020
DeepClean: One-dimensional Convolutional Neural Network which takes a specified set of witness channels and subsequently outputs the predicted noise in strain.
IGWN data processing
Non-stationary
Non-Gaussianity
Background
Related Works
Model Structure
Precessing & Train
Effect on Noise
Effect on BBH signals
Credit: Marco Cavaglià
["This", "is", "a", "sample"]
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
Strain
Whiten
Normalized
∼\(10^{−19}\)
∼\(10^{2}\)
∼\(10^{0}\)
32 s
32 s
merger
\(t_c\) (around GW150914)
(Cal network SNR)
Band-pass: [20, 2048] Hz
Patching (tokenized) with size 0.125 s and overlap 50%
[1, 128, 256]
(Standard normalization)
dynamic masking
[1, 16512]
[1, 128, 256]
(PSD\(_i\) from noise)
Band-pass: [20, 2048] Hz
WaveFormer
MSE-Loss\(_i\)
\(std\)
[1, 128, 256]
Noise\(_i\):
Signal\(_i\):
Input\(_i\):
Label\(_i\):
Output\(_i\):
8.0625 s
8.0625 s
Given �=ℎ+�d=h+n, we can normalize �d as follows:
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
(Bottom panels: results of glitches)
(Upper panels: results of pure noise)
Time-series and spectrogram example of blip.
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
(Upper panels: Signal amplitude recovery performance
(Bottom panels: Signal phase recovery performance)
Bacon P. et al. arXiv: 2205.13513
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
GW191204_171526
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
Firstly, we obtain the denoised output by utilizing Waveformer. Then, triggers are defined and identified by three steps including,
Find Peaks. Locate triggers on a single detector by finding its maximum all local-maximum (0.2s away from neighboring maximum/local-maximum).
An search algorithm for GW require that: [cite: 2010.07244]
the same signal is seen in the detectors; (the same signal is seen by time-shifting in single detector)
the same waveform must be present both detectors;
and the signal’s time of arrival must be consistent with the GW travel time between the observatories.
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
Firstly, we obtain the denoised output by utilizing Waveformer.
Then, triggers are defined and identified by three steps including:
Find Peaks. Locate triggers on a single detector by finding its maximum all local-maximum (0.2s away from neighboring maximum/local-maximum).
By constraining triggers that exist on both two detectors, we get VALID triggers. (consist 3~4 segments)
Calculate the cross-correlation of the to-be-evaluated trigger across channels or within a single channel, between its noisy and corresponding denoised segments, as well as between denoised segments themselves.
noisy input segments
denoised output segments
\(\bar{H}\)
\(\bar{L}\)
\({H}\)
\({L}\)
AI
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
Firstly, we obtain the denoised output by utilizing Waveformer. Then, triggers are defined and identified by three steps including,
Find Peaks. Locate triggers on a single detector by finding its maximum all local-maximum (0.2s away from neighboring maximum/local-maximum).
By constraining triggers that exist on both two detectors, we get VALID triggers. (consist 3~4 segments)
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
Firstly, we obtain the denoised output by utilizing Waveformer. Then, triggers are defined and identified by three steps including,
Find Peaks. Locate triggers on a single detector by finding its maximum all local-maximum (0.2s away from neighboring maximum/local-maximum).
By constraining triggers that exist on both two detectors, we get VALID triggers. (consist 3~4 segments)
Calculate the correlation of the to-be-evaluated trigger across channels or within a single channel, between its noisy and corresponding denoised segments, as well as between denoised segments themselves.
noisy input segments
denoised output segments
\(\bar{H}\)
\(\bar{L}\)
\({H}\)
\({L}\)
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
Firstly, we obtain the denoised output by utilizing Waveformer. Then, triggers are defined and identified by three steps including,
Find Peaks. Locate triggers on a single detector by finding its maximum all local-maximum (0.2s away from neighboring maximum/local-maximum).
By constraining triggers that exist on both two detectors, we get VALID triggers. (consist 3~4 segments)
Calculate the correlation of the to-be-evaluated trigger across channels or within a single channel, between its noisy and corresponding denoised segments, as well as between denoised segments themselves.
OURs
(PyCBC) Davies, et al. PRD 2020
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
Assessed denoising workflow performance by comparing with GWTC-1, GWTC-2, GWTC2.1, and GWTC-3 catalogs and associated data releases.
Noted significant divergence in IFAR distribution between our results and those from GWTC and OGC catalogs.
Achieved significant IFAR improvement across all 75 reported BBH events, indicating effective suppression of loud terrestrial noise.
Example: For low SNR (\(10.8_{-0.4}^{+0.3}\)) event GW200208_130117, obtained an IFAR of 8916 years, surpassing maximum IFAR of <4000 years in other catalogs.
Variability in IFAR improvement linked to the original data's noise nature, including its non-Gaussian, non-stationary characteristics, and different signal recognition strategies by pipelines.
IFAR performance significantly depends on the reduction of non-Gaussian noise near each event.
Events with substantial IFAR improvement had misleading non-Gaussian noise effectively eliminated.
Events where IFAR underperforms retained non-Gaussian characteristics, possibly due to WaveFormer's inherent systematic errors.
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
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).
Menéndez-Vázquez A, et al. PRD 2021
Alfaidi & Messerger. arXiv:2402.04589
The negative log-likelihood cost function always strongly penalizes the most active incorrect prediction. And the correctly classified examples will contribute little to the overall training cost."
—— I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. 2016. (book)
noise
noise + signal
OURs
LVK. PRD (2016). arXiv:1602.03839
GW151226
GW151012
Exploring Beyond General Relativity
Harsh Narola, et al. “Beyond General Relativity: Designing a Template-Based Search for Exotic Gravitational Wave Signals.” PRD 107, 2 (2023): 024017.
Yu-Xin Wang, et al. "Draft in Progress"
iFAR [years]
iFAR [years]
Sensitivity dfistance [Mpc]
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 \(\mathbf{y}\in\mathbb{R}^D\) as a transformation \(T\) of a real vector \(\mathbf{z}\in\mathbb{R}^D\) sampled from \(p_{\mathrm{z}}(\mathbf{z})\):
Note: The invertible and differentiable transformation \(T\) and the base distribution \(p_{\mathrm{z}}(\mathbf{z})\) can have parameters \(\{\boldsymbol{\phi}, \boldsymbol{\psi}\}\) of their own, i.e. \( T_{\phi} \) and \(p_{\mathrm{z},\boldsymbol{\psi}}(\mathbf{z})\).
Change of Variables:
Equivalently,
The Jacobia \(J_{T}(\mathbf{u})\) is the \(D \times D\) 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
DINGO 要特别快,要足够准
(缺DINGO技术相关的前沿动态)
Enrico 只要够快就足够了,可以做引力检验
(调研所有和最新的AI+引力检验文章)
(缺PyGWB的引入、数据描述、结果)-> 同样遇到如何做statistics的问题
pure signals of SGWB
pure noise
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)
Exploring Stochastic Gravitational Wave Background with AI
Our result (preliminary)
Exploring Stochastic Gravitational Wave Background with AI
Abbott R, et al. PRD 104, 2 (2021): 022004.
PyGWB result
Our result (preliminary)
AI or Bayes
Text-to-image
"A running dog"
AI or Bayes
Text-to-image
"A corgi running on the street"
A picture is worth a thousand words.
A fraction of a thousand words.
Credit: 李宏毅
"A running dog"
AI vs Classical Methods
Alfaidi & Messerger. arXiv:2402.04589
Our result (preliminary)
Menéndez-Vázquez A, et al. PRD 2021
PyGWB result
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
Statistics
Statistics
AI vs Classical Methods
On-going
Insights
Statistics
Statistics
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')
This silde: https://slides.com/iphysresearch/2024july_lzu
Nature Physics 18, 1 (2022): 9–11
On-going
Insights
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')
For further reference or to cite the work presented today,
please cite this silde: https://slides.com/iphysresearch/2024mar_bnuz
https://twitter.com/chipro/status/1768388213008445837?s=46&t=JmDXWgIucgr_FlsBFTvuRQ
如何把大模型这个概念在合适的地方讲出来?(开头?最后?)
高维、多模态的inference挑战(是整个引力波相关的科学研究的技术难点)
PTMCMC 的算法描述图
Multi-agent 的概念图,及其相关结果的对比图
DINGO: A Leap Forward
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)
[1]. https://github.com/stephengreen/lfi-gw (published @2020)
[2]. https://github.com/dingo-gw/dingo (published @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 \(10^2\) to \(10^3\), 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 \(f_1\), then to the TDI outputs through a time-independent mapping \(f_2\), and an exponential term.
TDI-A
These steps can be schematically summarized as:
where \(\mathcal{T}_\alpha^{A, E}(f)\) 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 \(\boldsymbol{\theta}_S\) 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 \(f_2\), 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 \(f_2\).
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 \(f_1\), then to the TDI outputs through a time-independent mapping \(f_2\), 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 (\(t_c\)), 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
Earth-based GW detection (\(\sim10^2\)Hz)
Space-based GW detection (\(\sim10^{-3}\)Hz)
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)
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')
Earth-based GW detection
Space-based GW detection
This silde: https://slides.com/iphysresearch/2024Aug_ihep
Calibration Analysis on Denoised / Residual Data
He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046
M. Du, B. Liang, HW*, P. Xu, Z. Luo, Y. Wu*. SCPMA 67, 230412 (2024).
Neural density estimation
Ref:
Neural density estimation
nflow
“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。
引力波天文学 & 引力波多信使天文学
Chinese Journal of Space Science, 2023, 43(4): 589-599.
LIGO-G2300554
Nat. Astron. 2021, 5(9): 881-889.
空基引力波探测科学数据的分析与地基相比差距很大:
空间引力波观测频段内含有大量的波源和多种波源类型:
空间太极计划
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
Credit: ESA, K. Holley-Bockelmann
天琴计划
空基引力波探测科学数据的分析与地基相比差距很大:
空间引力波观测频段内含有大量的波源和多种波源类型:
天琴计划
Credit: ESA, K. Holley-Bockelmann
Credit: Minghui Du
空间太极计划
(Sec.8.3.1 Red Book)
空间引力波探测获得的是什么样的 (科学) 数据?
Analyses cannot treat sources independently and sequentially work through a list of candidate detections.
空间引力波探测获得的是什么样的 (科学) 数据?
其他重要的科学数据处理的技术挑战:
随机噪声(Stochastic noise)
仪器瞬变(glitches)
频谱线(Spectral lines)
数据间断(Data gaps)
非平稳性(Non-stationarities)
Baghi et al., Phys. Rev. D (2019)
Analyses cannot treat sources independently and sequentially work through a list of candidate detections.
Mock Data Challenges
波源模板
MNRAS 488, L94–L98 (2019)
EMRI 波形模板需求量 40 个数量级以上
Marsat et al. PRD 103, 8 (2021)
Our results indicate that the existing numerical relativity waveforms are as accurate as 99% with respect to space-based detectors including LISA, Taiji and Tianqin. Such accuracy level is comparable to the one with respect to LIGO.
(ZW, JJZ, ZJC, arXiv:2401.15331)
Bayes' theorem:
探测器响应
在时域中计算的挑战性在于,在每个采样点处都需要计算波形和响应,如果考虑到不同的 TDI 组合方式,则计算的时间复杂度将进一步增大。频域中的 TDI 响应形式,可简单概况为:
其中 \(\alpha \in\{+, \times\}\),如果要考虑高阶模的贡献,则 \(\alpha=\ell m\) 。\(t_\alpha(f)\) 描述了时间与引力波瞬时频率的关系,可通过
计算, 其中 \(\Psi_\alpha\) 表示 \(\alpha\) 模式频域波形的相位。 \(\mathcal{T}\) 对时间的依赖关系反映了探测器轨道运动对信号的调制效应,如右图所示。调制效应为响应的建模和计算增加了复杂性,但同时也有助于在参数估计中解除外禀参数之间的简并,提升对波源的定位精度。
Bayes' theorem:
TDI-A
TDI-A
TDI-E
TDI-T
Credit: Minghui Du
数据噪声
Addressing Instrumental Imperfections
数据间断(Data gaps)
瞬态噪声事件(glitches)
频谱线(Spectral lines)
非平稳性(Non-stationarities)
不完美校准(imperfect calibration)
(Sec.8.3.3 Red Book)
Sasli et al., Phys. Rev. D (2023)
Baghi et al., Phys. Rev. D (2019)
似然函数建模
Bayes' theorem:
搜索技术
UCB
GPU-based
...
Karnesis et al. 2303.02164.
Hoy & Nuttall. 2312.13039.
Weaving et al. CQG 41, (2023)
Strub et al., PRD. arXiv:2307.03763
Bayes' theorem:
Nat. Astron. 2022, 6(12): 1356-1363.
Nat. Astron. 2022, 6(12): 1334-1338.
背景与挑战
全局拟合方法 (Global-fit method)
实践应用步骤
潜在局限性
全局拟合
Global-fit
全局拟合
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 |
Eryn | UCB | Python (TPMCMC / RJMCMC) | No code for UCB case |
PyCBC-INFERENCE | MBHB | Python (?) | Unavailable |
Bilby in Space / tBilby | MBHB / ? | ? / Python? (RJMCMC) | Unavailable |
Strub et al. | UCB | ? (GP) | Unavailable / GPU-based |
Zhang et al. (LZU) | UCB | ? (PSO) | MLP |
Bayes' theorem:
Nat. Astron. 2022, 6(12): 1334-1338.
Nat. Astron. 2022, 6(12): 1356-1363.
(Sec.8.6 Red Book)
超高维度的波源参数空间特性 (编码波形)
科学数据的动态性 (编码数据)
资源优化挑战 (CPU vs GPU)
F(t) over 1 year
h(t) over 10 min
y(t) over 1 year
多类型的大量波源混叠问题
Actually, there are more ...
Credit: Maude Le Jeune (2021)
MCMC采样的高效性和收敛性
multi-agentic reasoning
超高维度的波源参数空间特性 (编码波形)
科学数据的动态性 (编码数据)
资源优化挑战 (CPU vs GPU)
F(t) over 1 year
h(t) over 10 min
y(t) over 1 year
多类型的大量波源混叠问题
Actually, there are more ...
Credit: Maude Le Jeune (2021)
MCMC采样的高效性和收敛性
multi-agentic reasoning
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')
This silde: https://slides.com/iphysresearch/2024may_neu
Nat. Astron. 2022, 6(12): 1356-1363.
Nat. Astron. 2022, 6(12): 1334-1338.
背景与挑战
全局拟合方法 (Global-fit method)
实践应用步骤
局限性
全局拟合
Global-fit
Strub et al., PRD. arXiv:2307.03763
研究现状
UCB
GPU-based
...
Karnesis et al. 2303.02164.
Hoy & Nuttall. 2312.13039.
Weaving et al. CQG 41, (2023)
Littenberg & Cornish, Phys. Rev. D (2023)
技术痛点/局限性
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”
2023年,DeepMind发布AI工具GNoME (Nature),成功预测220万种晶体结构
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 + 量子、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
验证数学猜想
基于 CNN 的开创性研究工作
卷积神经网络 (CNN) 可以用来搜寻双黑洞并合系统所产生的引力波信号
灵敏度:与匹配滤波方法可比拟
执行速度:远胜过匹配滤波方法 (有GPU加持)
PRL, 2018, 120(14): 141103.
PRD, 2018, 97(4): 044039.
AI for Science \(\rightarrow\) AI for GW Astronomy
Exported: Oct, 2023 (in preparation)
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)
\(h(\theta):=\sum_i \alpha_i(\theta) e_i \equiv \alpha(\theta) \text {, }\)
where \(\alpha\in\mathbb{C}^{241}\) and reduced basis \(\{e_i\}\) with \(\left\langle e_i \mid e_j\right\rangle=\delta_{i j}\).
深度学习算法的学习目标:
\((\mathcal{M}_c, \eta)\in\Theta\subset\mathbb{R}^2\)
\((\alpha_r, \alpha_i) \in\mathbb{R}^{482} \)
Neural
Network
AAK - FastEMRIWaveforms (FEW)
Katz et al., Phys. Rev. D (2021)
Chua et al., Phys. Rev. Lett., (2021)
~1s (快 ≳\(10^4\) 倍)
MNRAS 488, L94–L98 (2019)
EMRI 波形模板需求量 40 个数量级以上
张雪婷, C. Messenger, N. Korsakova,
ML Chan, 胡一鸣, 张建东, Phys. Rev. D (2022)
赵天宇, 周阅, 施锐俊, 曹周键, 任智祥, arXiv:2308.16422
恽倩芸, 韩文标, 郭意扬, 王赫, 杜明辉, arXiv:2309.06694
Zhang et al. PRD (2022) |
Zhao et al. (2308.16422) |
Yun et al. (2309.06694) |
|
---|---|---|---|
TDI | - | TDI-1.5 | TDI-2.0 |
Duration | 3 months | 1 year | 0.5 year |
Waveform Family (train) | AK | AAK | AAK |
Waveform Family (test) | AK / AAK | AK / AAK | AAK |
GW Project | TianQin | LISA | Taiji |
Acceleration Noise [fm/sqrt(Hz)] | 1 | 3 | 3 |
OMS Noise [pm/sqrt(Hz)] | 1 | 15 | 8 |
Base Model | CNN | CNN | CNN |
Input Feature domain | time | frequency | time-frequency |
sampling rate | 1/30 Hz | 1/15 Hz | 1/10 Hz |
Yun et al. (2311.18640) |
---|
TDI-2.0 |
0.5 year |
AAK |
AAK / EOB |
Taiji |
3 |
8 |
Unet / VGG |
time-frequency |
1/10 Hz |
Zhang et al. PRD (2022) |
Zhao et al. (2308.16422) |
Yun et al. (2309.06694) |
|
---|---|---|---|
TDI | - | TDI-1.5 | TDI-2.0 |
Duration | 3 months | 1 year | 0.5 year |
Waveform Family (train) | AK | AAK | AAK |
Waveform Family (test) | AK / AAK | AK / AAK | AAK |
GW Project | TianQin | LISA | Taiji |
Acceleration Noise [fm/sqrt(Hz)] | 1 | 3 | 3 |
OMS Noise [pm/sqrt(Hz)] | 1 | 15 | 8 |
Base Model | CNN | CNN | CNN |
Input Feature domain | time | frequency | time-frequency |
sampling rate | 1/30 Hz | 1/15 Hz | 1/10 Hz |
赵天宇*, Ruoxi Lyu*, 王赫, 曹周键, 任智祥, Commun. Phys., (2023)
"One Model to Rule Them All":EMRI / MBHB / GBs / SGWB 的信号提取
王赫, 吴仕超, 曹周键, 刘骁麟, 朱建阳,
Phys. Rev. D, (2020)
阮文洪*, 王赫*, 刘畅, 郭宗宽,
Phys. Lett. B, (2023)
LDC 一年数据上对 MBHB (+GBs) 信号的信号探测
杜明辉*, 梁博*, 王赫†, 徐鹏, 罗子人, 吴岳良†, accepted by SCPMA, arXiv:2308.05510
阮文洪, 王赫, 刘畅, 郭宗宽,
Universe (2023)
亮点:
杜明辉*, 梁博*, 王赫†, 徐鹏, 罗子人, 吴岳良†, accepted by SCPMA, arXiv:2308.05510
阮文洪, 王赫, 刘畅, 郭宗宽,
Universe (2023)
亮点:
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')
This silde: https://slides.com/iphysresearch/2024may_neu
归一化流模型示意图
亮点:
超高维度的波源参数空间特性 (编码波形)
科学数据的动态性 (编码数据)
计算资源的需求与利用 (CPU vs GPU)
F(t) over 1 year
h(t) over 10 min
y(t) over 1 year
Credit: Maude Le Jeune (2021)
Actually, there are more ...
多类型的大量波源混叠问题
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')
This slide: https://slides.com/iphysresearch/2024jan_bnu
超高维度的波源参数空间特性 (编码波形)
科学数据的动态性 (编码数据)
计算资源的需求与利用 (CPU vs GPU)
F(t) over 1 year
h(t) over 10 min
y(t) over 1 year
Credit: Maude Le Jeune (2021)
Actually, there are more ...
多类型的大量波源混叠问题
EMRI 波形模板需求量 40 个数量级以上
MNRAS 488, L94–L98 (2019)
Chua et al., Phys. Rev. Lett., (2021)
~1s (快 ≳\(10^4\) 倍)
\(h(\theta):=\sum_i \alpha_i(\theta) e_i \equiv \alpha(\theta) \text {, }\)
where \(\alpha\in\mathbb{C}^{241}\) and reduced basis \(\{e_i\}\) with \(\left\langle e_i \mid e_j\right\rangle=\delta_{i j}\).
深度学习算法的学习目标
\((\mathcal{M}_c, \eta)\in\Theta\subset\mathbb{R}^2\)
\((\alpha_r, \alpha_i) \in\mathbb{R}^{482} \)
Neural
Network
Chua et al., PRL 122, 21 (2019): 211101.
Chua & Vallisneri. PRL 124, 4 (2020): 041102.
Katz et al., PRD 104, 6 (2021): 064047.
AKK - FastEMRIWaveforms (FEW) package
Katz et al., Phys. Rev. D (2021)
时长 1 年且含有信噪比 70的EMRI时域数据
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
H.W., et al. Big Data Min. Anal. (2021)
PRD 108, 4 (2023): 044029.
Neural Posterior Estimation with Guaranteed Exact Coverage: The Ringdown of GW150914
arXiv:2310.12209
Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows
arXiv:2310.13405, LIGO-P2300306
Cosmological Inference using Gravitational Waves and Normalising Flows
PRL 131, 17 (2023): 171403.
Angular Power Spectrum of Gravitational-Wave Transient Sources as a Probe of the Large-Scale Structure
On-going
Insights
Dynamic training samples in memory.
Nature Physics 18, 1 (2022): 9–11
On-going
Insights
Dynamic training samples in memory.
Nature Physics 18, 1 (2022): 9–11
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')
This slide: https://slides.com/iphysresearch/2024jan_bnu
空间引力波探测的典型波源与全局拟合问题
GW sources and the global-fit problem
其他科学数据处理挑战:Glitches, gaps, non-stationarities
空间引力波探测科学数据处理的技术挑战(超高维参数空间(内禀),非稳态,计算资源)
科学数据处理的深度学习技术应用:现状
科学数据处理的深度学习技术应用:现状
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
©Floor Broekgaarden (repo)
GWTC-3
—— Bernard F. Schutz
DOI:10.1063/1.1629411
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
Matched-filtering Convolutional Neural Network (MFCNN)
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.
LIGO-Virgo data processing
PRD 107, 6 (2023) 063029
Ensemble learning leverages statistical approaches to utilize more information for making informed decisions by combining multiple models.
PRD 101, 10 (2020) 104003.
PRD 105, 8 (2022) 083013
PRD 107, 2 (2023): 023021.
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)
Data quality improvement
Credit: Marco Cavaglià
LIGO-Virgo data processing
GW searches
Astrophsical interpretation of GW sources
LIGO-Virgo data processing
Nature Physics 18, 1 (2022) 112–17
Big Data Mining and Analytics 5, 1 (2021) 53–63.
PRL 127, 24 (2021) 241103.
PRL 130, 17 (2023) 171403.
arXiv:2212.14283
Data quality improvement
Credit: Marco Cavaglià
LIGO-Virgo data processing
GW searches
Astrophsical interpretation of GW sources
arXiv:2212.14283
LIGO-Virgo data processing
arXiv:2212.14283
LIGO-Virgo data processing
Taiji
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
Credit: ESA, K. Holley-Bockelmann
PLB 841 (2023) 137904.
Communications Physics, 2023, 6(1): 212.
arXiv:2308.05510
Universe, 2023, 9(9): 407.
Schematic view of the global fit approach.
GW170817
GW190412
GW190814
Bayes factor (MCMC)
PRD 101, 10 (2020) 104003.
(In preparation)
arXiv:2305.18528
ICML2023
Bayes
AI
Credit: 李宏毅
Text-to-image
Bayes
AI
Credit: 李宏毅
Text-to-image
for _ in range(num_of_audiences):
print('Thank you for your attention! 🙏')
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。
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
By He Wang
2024.10.28 @武汉大学