2024年粒子天体物理重点实验室系列学术报告|8月19日上午10:00 @高能所

Enhancing Gravitational Wave Astronomy with Artificial Intelligence

He Wang (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

Enhancing Gravitational Wave Astronomy with Artificial Intelligence

He Wang (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

Content

GW Astronomy
AI for Science · GW Data Analysis
GW search · Pipeline
Parameter estimation · Scientific discovery
Key Takeaways
(Space-based GW Detection)

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

Gravitational Wave Astronomy

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

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

The first GW event of GW150914

Detecting gravitational waves require a mix of FIVE key ingredients:
1. good detector technology
2. good waveform predictions
3. good data analysis methodology and technology
4. coincident observations in several independent detectors
5. coincident observations in electromagnetic astronomy

—— Bernard F. Schutz

DOI: 10.1063/1.1629411

GWTC-3

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:
1. good detector technology
2. good waveform predictions
3. good data analysis methodology and technology
4. coincident observations in several independent detectors
5. coincident observations in electromagnetic astronomy

—— Bernard F. Schutz

DOI: 10.1063/1.1629411

GWTC-3

Gravitational Wave Astronomy

©Floor Broekgaarden (repo)

Technical Challenges: Data Processing for GW

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
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).

Text

LIGO-VIRGO-KAGRA

LISA / Taiji project

Text

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 MF
Bayesian hypothesis testing:
- start from the same likelihood
- define a prior over signal parameters
- marginalise over them to arrive at a Bayes factor
- Often the dirty secret: just treat this as a Frequentist detection statistic
  $\rightarrow$ recover the MF (for certain prior choices)

科学智能：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
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

Text

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.

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.

Text

Exported: Oct, 2023 (in preparation)

PRL, 2018, 120(14): 141103.

PRD, 2018, 97(4): 044039.

引力波数据处理：人工智能技术应用

Content

GW Astronomy
AI for Science · GW Data Analysis
GW search · Pipeline
Parameter estimation · Scientific discovery
Key Takeaways
(Space-based GW Detection)

GW search · Pipeline

Text

Introduction to Speed and Efficiency

Machine Learning (ML) offers unparalleled speed in GW detection.
The increasing sensitivity to noise and the growing number of GW events demand faster analysis capabilities.

The Need for Integration (an AI pipeline!)

Numerous AI algorithms exist for signal detection (see more: https://wiki.ligo.org/MLA/ML_at_LIGO_and_VIRGO), highlighting the need for a streamlined pipeline.
However, none are operational in O4 due to infrastructure challenges and the lack of standardized sensitivity measures against non-stationary noise.

Case study: Pipeline

The Aframe online pipeline (arXiv:2403.18661) represents a significant step forward.
Utilizes GPU acceleration for signal preprocessing, model inference, and real-time false alarm rate calculation.

Text

Aframe

S.S. Chaudhary, et al. arXiv:2308.04545

GW search · Pipeline

Text

Challenges and Future Directions

A comprehensive online pipeline should consider various signal types, including BNS and NSBH, and all detectors, including Virgo and KAGRA.
Beyond false alarm rates, additional real-time information is crucial for a holistic analysis.

Case study: Pipeline

The Aframe online pipeline (arXiv:2403.18661) represents a significant step forward.

Despite its advancements, it primarily focuses on BBH signals, with decreased performance on longer signals like BNS, a point also highlighted in MLGWSC1(PRD, 2209.11146).

Text

Aframe

OpenLVEM, June 08, 2023. Low Latency UPDATE.

Matched-filtering Convolutional Neural Network (MFCNN)

Text

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).

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)

Text

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)

Text

Expanding the dimension of the output

is to call more information to make decisions in improving AI models.

Text

CL.M., W.W., H.W., et al. PRD (2023)

GW search · Pipeline

Text

Beyond Speed: Generalization and Discovery in GW Detection

Our primary goal is not speed but the model's ability to generalize and discover new GW signals, including those beyond the reach of matched filtering techniques and General Relativity (GR).
Leveraging our experience in signal modeling (MFCNN) and noise modeling (WaveFormer), we are gradually building an offline pipeline capable of searching for signals in complete GW observation data and calculating FARs.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012)

Feature extraction

Convolutional Neural Network (ConvNet or CNN)

Classification

GW150914

GW151226

GW151012

CNNs always works pretty good on stimulated noises.
However, when on real noises from LIGO, this approach does not work that well.
It's too sensitive against the background + hard to find GW events)

>> Is it matched-filtering ?

>> Wait, It can be matched-filtering!

GW search · Pipeline

Text

Beyond Speed: Generalization and Discovery in GW Detection

Our primary goal is not speed but the model's ability to generalize and discover new GW signals, including those beyond the reach of matched filtering techniques and General Relativity (GR).
Leveraging our experience in signal modeling (MFCNN) and noise modeling (WaveFormer), we are gradually building an offline pipeline capable of searching for signals in complete GW observation data and calculating FARs.

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.

In practice, we use matched filters as an essential component of feature extraction in the first part of the CNN for GW detection.

Real-time GW searches for GW150914

GW search · Pipeline

Text

Beyond Speed: Generalization and Discovery in GW Detection

Our primary goal is not speed but the model's ability to generalize and discover new GW signals, including those beyond the reach of matched filtering techniques and General Relativity (GR).
Leveraging our experience in signal modeling (MFCNN) and noise modeling (WaveFormer), we are gradually building an offline pipeline capable of searching for signals in complete GW observation data and calculating FARs.

He Wang, et al. PRD 101, 10 (2020): 104003

Transform matched-filtering method from frequency domain to time domain.
The square of matched-filtering SNR for a given data $d(t) = n(t)+h(t)$:

Frequency domain

\langle h|h \rangle = 4\int^\infty_0\frac{\tilde{h}(f)\tilde{h}^*(f)}{S_n(f)}df

\langle d|h \rangle (t) = 4\int^\infty_0\frac{\tilde{d}(f)\tilde{h}^*(f)}{S_n(f)}e^{2\pi ift}df

\rho^2(t)\equiv\frac{1}{\langle h|h \rangle}|\langle d|h \rangle(t)|^2

\int\tilde{x}_1(f) \cdot \tilde{x}_2(f) e^{2\pi ift}df= x_1(t)*x_2(t)

x_1(t)*x_2^*(-t) = x_1(t)\star x_2(t)

\int\tilde{x}_1(f) \cdot \tilde{x}^*_2(f) e^{2\pi ift}df= x_1(t)\star x_2(t)

GW search · Pipeline

Text

Beyond Speed: Generalization and Discovery in GW Detection

Our primary goal is not speed but the model's ability to generalize and discover new GW signals, including those beyond the reach of matched filtering techniques and General Relativity (GR).
Leveraging our experience in signal modeling (MFCNN) and noise modeling (WaveFormer), we are gradually building an offline pipeline capable of searching for signals in complete GW observation data and calculating FARs.

He Wang, et al. PRD 101, 10 (2020): 104003

Transform matched-filtering method from frequency domain to time domain.
The square of matched-filtering SNR for a given data $d(t) = n(t)+h(t)$:

Frequency domain

\langle h|h \rangle = 4\int^\infty_0\frac{\tilde{h}(f)\tilde{h}^*(f)}{S_n(f)}df

\langle d|h \rangle (t) = 4\int^\infty_0\frac{\tilde{d}(f)\tilde{h}^*(f)}{S_n(f)}e^{2\pi ift}df

\rho^2(t)\equiv\frac{1}{\langle h|h \rangle}|\langle d|h \rangle(t)|^2

\int\tilde{x}_1(f) \cdot \tilde{x}_2(f) e^{2\pi ift}df= x_1(t)*x_2(t)

x_1(t)*x_2^*(-t) = x_1(t)\star x_2(t)

\int\tilde{x}_1(f) \cdot \tilde{x}^*_2(f) e^{2\pi ift}df= x_1(t)\star x_2(t)

Time domain

(normalizing)

(matched-filtering)

\langle h|h \rangle \sim [\bar{h}(t) \ast \bar{h}(-t)]|_{t=0}

\langle d|h \rangle (t) \sim \,\bar{d}(t)\ast\bar{h}(-t)

$S_n(|f|)$ is the one-sided average PSD of $d(t)$

(whitening)

where

\bar{S_n}(t)=\int^{+\infty}_{-\infty}S_n^{-1/2}(f)e^{2\pi ift}df

\left\{\begin{matrix} \bar{d}(t) = d(t) * \bar{S}_n(t) \\ \bar{h}(t) = h(t) * \bar{S}_n(t) \end{matrix}\right.

GW search · Pipeline

Text

Beyond Speed: Generalization and Discovery in GW Detection

Our primary goal is not speed but the model's ability to generalize and discover new GW signals, including those beyond the reach of matched filtering techniques and General Relativity (GR).
Leveraging our experience in signal modeling (MFCNN) and noise modeling (WaveFormer), we are gradually building an offline pipeline capable of searching for signals in complete GW observation data and calculating FARs.

He Wang, et al. PRD 101, 10 (2020): 104003

Transform matched-filtering method from frequency domain to time domain.
The square of matched-filtering SNR for a given data $d(t) = n(t)+h(t)$:

Frequency domain

\langle h|h \rangle = 4\int^\infty_0\frac{\tilde{h}(f)\tilde{h}^*(f)}{S_n(f)}df

\langle d|h \rangle (t) = 4\int^\infty_0\frac{\tilde{d}(f)\tilde{h}^*(f)}{S_n(f)}e^{2\pi ift}df

\rho^2(t)\equiv\frac{1}{\langle h|h \rangle}|\langle d|h \rangle(t)|^2

\int\tilde{x}_1(f) \cdot \tilde{x}_2(f) e^{2\pi ift}df= x_1(t)*x_2(t)

x_1(t)*x_2^*(-t) = x_1(t)\star x_2(t)

\int\tilde{x}_1(f) \cdot \tilde{x}^*_2(f) e^{2\pi ift}df= x_1(t)\star x_2(t)

Time domain

(normalizing)

(matched-filtering)

\langle h|h \rangle \sim [\bar{h}(t) \ast \bar{h}(-t)]|_{t=0}

\langle d|h \rangle (t) \sim \,\bar{d}(t)\ast\bar{h}(-t)

$S_n(|f|)$ is the one-sided average PSD of $d(t)$

(whitening)

where

\bar{S_n}(t)=\int^{+\infty}_{-\infty}S_n^{-1/2}(f)e^{2\pi ift}df

\left\{\begin{matrix} \bar{d}(t) = d(t) * \bar{S}_n(t) \\ \bar{h}(t) = h(t) * \bar{S}_n(t) \end{matrix}\right.

Deep Learning Framework

In the 1-D convolution ($*$) on Apache MXNet, given input data with shape [batch size, channel, length] :

output[n, i, :] = \sum^{channel}_{j=0} input[n,j,:] \ast weight[i,j,:]

FYI: $N_\ast = \lfloor(N-K+2P)/S\rfloor+1$

（A schematic illustration for a unit of convolution layer)

GW search · Pipeline

Text

Beyond Speed: Generalization and Discovery in GW Detection

Our primary goal is not speed but the model's ability to generalize and discover new GW signals, including those beyond the reach of matched filtering techniques and General Relativity (GR).
Leveraging our experience in signal modeling (MFCNN) and noise modeling (WaveFormer), we are gradually building an offline pipeline capable of searching for signals in complete GW observation data and calculating FARs.

He Wang, et al. PRD 101, 10 (2020): 104003

Transform matched-filtering method from frequency domain to time domain.
The square of matched-filtering SNR for a given data $d(t) = n(t)+h(t)$:

Frequency domain

\langle h|h \rangle = 4\int^\infty_0\frac{\tilde{h}(f)\tilde{h}^*(f)}{S_n(f)}df

\langle d|h \rangle (t) = 4\int^\infty_0\frac{\tilde{d}(f)\tilde{h}^*(f)}{S_n(f)}e^{2\pi ift}df

\rho^2(t)\equiv\frac{1}{\langle h|h \rangle}|\langle d|h \rangle(t)|^2

\int\tilde{x}_1(f) \cdot \tilde{x}_2(f) e^{2\pi ift}df= x_1(t)*x_2(t)

x_1(t)*x_2^*(-t) = x_1(t)\star x_2(t)

\int\tilde{x}_1(f) \cdot \tilde{x}^*_2(f) e^{2\pi ift}df= x_1(t)\star x_2(t)

Time domain

(normalizing)

(matched-filtering)

\langle h|h \rangle \sim [\bar{h}(t) \ast \bar{h}(-t)]|_{t=0}

\langle d|h \rangle (t) \sim \,\bar{d}(t)\ast\bar{h}(-t)

$S_n(|f|)$ is the one-sided average PSD of $d(t)$

(whitening)

where

\bar{S_n}(t)=\int^{+\infty}_{-\infty}S_n^{-1/2}(f)e^{2\pi ift}df

\left\{\begin{matrix} \bar{d}(t) = d(t) * \bar{S}_n(t) \\ \bar{h}(t) = h(t) * \bar{S}_n(t) \end{matrix}\right.

Deep Learning Framework

GW search · Pipeline

Text

Beyond Speed: Generalization and Discovery in GW Detection

Our primary goal is not speed but the model's ability to generalize and discover new GW signals, including those beyond the reach of matched filtering techniques and General Relativity (GR).
Leveraging our experience in signal modeling (MFCNN) and noise modeling (WaveFormer), we are gradually building an offline pipeline capable of searching for signals in complete GW observation data and calculating FARs.

He Wang, et al. PRD 101, 10 (2020): 104003

Time domain

(normalizing)

(matched-filtering)

\langle h|h \rangle \sim [\bar{h}(t) \ast \bar{h}(-t)]|_{t=0}

\langle d|h \rangle (t) \sim \,\bar{d}(t)\ast\bar{h}(-t)

$S_n(|f|)$ is the one-sided average PSD of $d(t)$

(whitening)

where

\bar{S_n}(t)=\int^{+\infty}_{-\infty}S_n^{-1/2}(f)e^{2\pi ift}df

\left\{\begin{matrix} \bar{d}(t) = d(t) * \bar{S}_n(t) \\ \bar{h}(t) = h(t) * \bar{S}_n(t) \end{matrix}\right.

Deep Learning Framework

modulo-N circular convolution

GW search · Pipeline

Text

Beyond Speed: Generalization and Discovery in GW Detection

Our primary goal is not speed but the model's ability to generalize and discover new GW signals, including those beyond the reach of matched filtering techniques and General Relativity (GR).
Leveraging our experience in signal modeling (MFCNN) and noise modeling (WaveFormer), we are gradually building an offline pipeline capable of searching for signals in complete GW observation data and calculating FARs.

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

Content

GW Astronomy
AI for Science · GW Data Analysis
GW search · Pipeline
- Data Denoising for Signal Detection
Parameter estimation · Scientific discovery
Key Takeaways
(Space-based GW Detection)

1400Ripples Air Compressor Blip

Extremely Loud Helix Koi Fish

Various types of Glitch

Background

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.
- Traditional machine learning algorithms Powell J, et al. CQG, 2015
- Deep learning algorithms Zevin, M, et al. C

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

CQG. 37 (2020) 055002

Network Architecture

The WaveFormer, a billion-scale transformer-based model, excels in suppressing realistic noise and recovering injections or GW events, thereby significantly improving data quality.
In its application, it treats each overlapping time-domain data subsequence as an individual token, akin to tokenization in natural language processing (NLP).

["This", "is", "a", "sample"]

He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046

Data Preprocessing and Training Strategy

\frac{d-mean}{std} = \frac{h}{std}+\frac{n-mean}{std}

Strain

Whiten

Normalized

∼$10^{−19}$

∼$10^{2}$

∼$10^{0}$

32 s

merger

$t_c$ (around GW150914)

\oplus

(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

Given $d = h + n$ , we can normalize $d$ as follows:

Implementations:
- PSD sampling from real noise.
- input size: 8.0625 sec
- fs = 2048Hz
- Band-pass: 20~2048Hz
- Masked loss

He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046

Effect on Realistic Noise

Noise level percentile amplitude is significantly reduced, by approximately two orders.
Further ASD analysis shows that WaveFormer effectively eliminates both narrowband and broadband spectral information, substantially lowering frequency contributions.
Using the Gravity Spy database for glitches with SNR > 10 and confidence > 0.95, results show significant suppression of glitches in real advanced LIGO-Virgo noise.

(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

Recovery of Binary Black Holes

Overlap and matched-filtering signal-to-noise are calculated to represent phase and amplitude recovery performance.
Among the intermediate frequency range (20–200 Hz) that covers rich BBH signal information, the ASD distribution of denoised waveform is evidently consistent with that of target signal.

(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

These results show that our denoising algorithm outperformed others by capturing the characteristic chirping morphology of BBH evolution, and can denoise signals in realistic detection scenarios without affecting signal characteristics such as phase and amplitude.
For the event GW191204_171526, classified as either an NSBH or a low-mass BBH candidate in GWTC-3, the overlap with IMRPhenomXPHM achieved 0.93 and 0.95 on H1 and L1, respectively, which are marked improvements over those achieved by BayesWave and cWB (with overlaps between 0.82–0.86).

GW191204_171526

Recovery of Binary Black Holes

He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046

Search Strategy Overview

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]
1. the same signal is seen in the detectors; (the same signal is seen by time-shifting in single detector)
2. the same waveform must be present both detectors;
3. 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

Search Strategy Overview

Firstly, we obtain the denoised output by utilizing Waveformer.
Then, triggers are defined and identified by three steps including:
1. Find Peaks. Locate triggers on a single detector by finding its maximum all local-maximum (0.2s away from neighboring maximum/local-maximum).
2. By constraining triggers that exist on both two detectors, we get VALID triggers. (consist 3~4 segments)
3. 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

$\bar{H}$

$\bar{L}$

${H}$

${L}$

\rho_\text{ranking}

He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046

Significance Estimates

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

Developed an AI-based workflow with WaveFormer, combining convolutional neural network and transformer for effective GW noise suppression and hierarchical feature extraction across a wide frequency range.
Achieved significant noise suppression and signal recovery performance improvements, including state-of-the-art results on real observational data and BBH events, leading to dramatic data quality improvement and significant IFAR enhancement on 75 reported BBH events.

Text

Challenges in Model Interpretability
- The black-box nature of AI models complicates interpretability, challenging the comparison of AI-generated detection statistics with traditional matched filtering chi-square distributions.
- Convincing the scientific community of the pipeline's validity and the statistical significance of new discoveries remains difficult despite the model's ability to identify potential gravitational wave signals.

OURs

LVK. PRD (2016). arXiv:1602.03839

GW151226

GW151012

GW search · Pipeline

He Wang et al 2024 Mach. Learn.: Sci. Technol. 5 015046

Content

GW Astronomy
AI for Science · GW Data Analysis
GW search · Pipeline
Parameter estimation · Scientific discovery
Key Takeaways
(Space-based GW Detection)

Parameter estimation · Scientific discovery

Credit: LIGO Magazine.

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!

AI for Gravitational Wave: Parameter Estimation

Bayesian statistics

Data quality improvement

Credit: Marco Cavaglià

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

CQG. 37 (2020) 055002

AI for Gravitational Wave: Parameter Estimation

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

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)】

Normalizing Flow Model (1/4)

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 $p_{\mathrm{z}}(\mathbf{z})$:

\mathbf{y}=T(\mathbf{z}) \quad \text { where } \quad \mathbf{z} \sim p_{\mathrm{y}}(\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:

p_{\mathrm{y}}(\mathbf{y})=p_{\mathrm{z}}(\mathbf{z})\left|\operatorname{det} J_{T}(\mathbf{z})\right|^{-1} \quad \text { where } \quad \mathbf{u}=T^{-1}(\mathbf{x}) .

J_{T}(\mathbf{z})=\left[\begin{array}{ccc} \frac{\partial T_{1}}{\partial \mathrm{z}_{1}} & \cdots & \frac{\partial T_{1}}{\partial \mathrm{z}_{D}} \\ \vdots & \ddots & \vdots \\ \frac{\partial T_{D}}{\partial \mathrm{z}_{1}} & \cdots & \frac{\partial T_{D}}{\partial \mathrm{z}_{D}} \end{array}\right]

Equivalently,

The Jacobia $J_{T}(\mathbf{u})$ is the $D \times D$ matrix of all partial derivatives of $T$ given by:

p_{\mathrm{y}}(\mathbf{y})=p_{\mathrm{z}}\left(T^{-1}(\mathbf{y})\right)\left|\operatorname{det} J_{T^{-1}}(\mathbf{y})\right|

p_{\mathrm{y}}(\mathbf{y})

p_{\mathrm{z}}(\mathbf{z})

\mathbf{z}

\mathbf{y}

T^{-1}

base density

target density

（Based on 1912.02762）

Normalizing Flow Model (2/4)

Data: target data $\mathbf{y}\in\mathbb{R}^{15}$ (with condition data $\mathbf{x}$).
Task:
- Fitting a flow-based model $p_{\mathrm{y}}(\mathbf{y} ; \boldsymbol{\theta})$ to a target distribution $p_{\mathrm{y}}^{*}(\mathbf{y})$
- by minimizing KL divergence with respect to the model’s parameters $\boldsymbol{\theta}=\{\boldsymbol{\phi}, \boldsymbol{\psi}\}$,
- where $\boldsymbol{\phi}$ are the parameters of $T$ and $\boldsymbol{\psi}$ are the parameters of $p_{\mathrm{z}}(\mathbf{z})=\mathcal{N}(0,\mathbb{I})$.
Loss function:
Assuming we have a set of samples $\left\{\mathbf{y}_{n}\right\}_{n=1}^{N}\sim p_{\mathrm{y}}^{*}(\mathbf{y})$,

Minimizing the above Monte Carlo approximation of the KL divergence is equivalent to fitting the flow-based model to the samples $\left\{\mathbf{y}_{n}\right\}_{n=1}^{N}$ by maximum likelihood estimation.

\mathcal{L}(\boldsymbol{\theta}) \approx-\frac{1}{N} \sum_{n=1}^{N} \log p_{\mathrm{z}}\left(T^{-1}\left(\mathbf{y}_{n} ; \boldsymbol{\phi}\right) ; \boldsymbol{\psi}\right)+\log \left|\operatorname{det} J_{T^{-1}}\left(\mathbf{y}_{n} ; \boldsymbol{\phi}\right)\right|+\mathrm{const.}

p_{\mathrm{y}}(\mathbf{y})

p_{\mathrm{z}}(\mathbf{z})

\mathbf{z}

\mathbf{y}

T^{-1}

base density

target density

\begin{aligned} \mathcal{L}(\boldsymbol{\theta}) &=D_{\mathrm{KL}}\left[p_{\mathrm{y}}^{*}(\mathbf{y}) \| p_{\mathrm{y}}(\mathbf{y} ; \boldsymbol{\theta})\right] \\ &=-\mathbb{E}_{p_{\mathbf{y}}^{*}(\mathbf{y})}\left[\log p_{\mathbf{y}}(\mathbf{y} ; \boldsymbol{\theta})\right]+\text { const. } \\ &=-\mathbb{E}_{p_{\mathbf{y}}^{*}(\mathbf{y})}\left[\log p_{\mathrm{z}}\left(T^{-1}(\mathbf{y} ; \boldsymbol{\phi}) ; \boldsymbol{\psi}\right)+\log \left|\operatorname{det} J_{T^{-1}}(\mathbf{y} ; \boldsymbol{\phi})\right|\right]+\mathrm{const} . \end{aligned}

\mathbb{E}_{p_{\mathbf{y}}^{*}(\mathbf{y})}\left[\log p_{\mathbf{y}}^{*}(\mathbf{y} ; \boldsymbol{\theta})\right]

Rational Quadratic Neural Spline Flows
(RQ-NSF)

Train

\vec\theta = (m_1,m_2,d_L, ...) \in P_{prior}

\vec{x}=\vec{h}_{\vec{\theta}} + \vec{n}

nflow

\vec{z} \Rightarrow \mathbb{N}(0,\mathbb{I})

Normalizing Flow Model (3/4)

归一化流模型示意图

Test

\vec\theta = (m_1,m_2,d_L, ...) \in P_{posterior}

\vec{x}=\vec{h}_{\vec{\theta}} + \vec{n}

nflow

\vec{z} \in \mathbb{N}(0,\mathbb{I})

Train

\vec\theta = (m_1,m_2,d_L, ...) \in P_{prior}

\vec{x}=\vec{h}_{\vec{\theta}} + \vec{n}

nflow

\vec{z} \Rightarrow \mathbb{N}(0,\mathbb{I})

Normalizing Flow Model (4/4)

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 $\Rightarrow$ TGR / Cosmology / PTA ...

Text

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

DOI: 10.1103/PhysRevLett.130.171402

Parameter Estimation · Scientific Discovery

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.

Text

Appreciating the Ringdown Overtone Test of GW150914

A notable work involves ringdown overtone testing, which, acknowledging the difficulty in achieving DINGO-like precision for complex waveforms, leverages the speed advantage of AI.
By simulating the signal and $10^3$ realizations of LIGO noise for each pixel, it accomplishes what is impossible for MCMC methods, prioritizing speed over precision in a strategic trade-off.

Parameter Estimation · Scientific Discovery

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/stephengreen/lfi-gw (2020)
https://github.com/dingo-gw/dingo (2023.03)

Parameter Estimation · Scientific Discovery

Parameter estimation · Scientific discovery

Text

Exploring Stochastic Gravitational Wave Background with AI

Utilizing AI for parameter estimation in the stochastic gravitational wave background (SGWB) presents a fascinating blend of rich theoretical content and the potential for optimizing current data processing methods.
While still preliminary and ongoing, our work shows promising results for high SNR SGWB scenarios, where AI-based posterior probabilities are notably more precise and narrower compared to traditional cross-correlation methods used in PyGWB.

\Omega_{\mathrm{GW}}(f)=\Omega_{\mathrm{ref}}\left(\frac{f}{f_{\mathrm{ref}}}\right)^\alpha

\Omega_{\mathrm{ref}}=10^{-6.1}

Our result (preliminary)

Parameter estimation · Scientific discovery

Text

Exploring Stochastic Gravitational Wave Background with AI

Performance saturation is observed between SNR levels of $10^{-6}$ to $10^{-7}$, indicating a plateau in model effectiveness in low SNR conditions.
Unlike PyGWB, which can accumulate cross-correlation data from SGWB to further constrain the power spectrum, AI model outputs do not readily provide statistically meaningful information for aggregation. Multiplying posterior probabilities from multiple segments leads to ambiguous, and potentially biased, results due to the lack of statistically significant fluctuations across different posterior distributions.

Abbott R, et al. PRD 104, 2 (2021): 022004.

PyGWB result

Our result (preliminary)

\Omega_{\mathrm{GW}}(f)=\Omega_{\mathrm{ref}}\left(\frac{f}{f_{\mathrm{ref}}}\right)^\alpha

Content

GW Astronomy
AI for Science · GW Data Analysis
GW search · Pipeline
Parameter estimation · Scientific discovery
Key Takeaways
(Space-based GW Detection)

Key Takeaways

Text

AI vs Classical Methods

In strict sense, at present AI methods have not yet become the perfect alternative to traditional methods (like MF, MCMC, etc.).
- There is a pressing need for the theoretical refinement of ML applications in GW statistics, aiming to bridge current gaps and enhance model reliability.
- How can we address the issue of strong or unacceptable biases that occur when outputs from AI models are used jointly or in combination to measure properties of a population, sub-population, or ensemble?
  (also addressed by 2405.18095)

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

Key Takeaways

Text

~~Statistics~~

\times N

~~Statistics~~

AI vs Classical Methods

In strict sense, at present AI methods have not yet become the perfect alternative to traditional methods (like MF, MCMC, etc.).
- There is a pressing need for the theoretical refinement of ML applications in GW statistics, aiming to bridge current gaps and enhance model reliability.
- How can we address the issue of strong or unacceptable biases that occur when outputs from AI models are used jointly or in combination to measure properties of a population, sub-population, or ensemble?
  (also addressed by 2405.18095)
How can we construct a gravitational wave statistical theory based on machine learning methods to achieve robust theoretical guarantees for detection statistics and statistical inference?
- $\rightarrow$ Is the goal to achieve results that are entirely consistent with the statistical properties of traditional methods, or
- $\rightarrow$ to quantify and calibrate any results provided by AI within the statistical theory framework?

Galactic Binary (GB) [$\mathcal{O}(10^4) \text{ in } \mathcal{O}(10^7)$]
Massive Black Hole Binary (MBHB) [$\mathcal{O}(2)\sim\mathcal{O}(10^2)$]
Extreme Mass-Ratio Inspiral (EMRI) [$\mathcal{O}(10)\sim\mathcal{O}(10^3)$]
Stellar-mass Black Hole Binary (SBHB)
Stochastic Gravitational Wave Background (SGWB)
Unmodelled sources (eg: Burst...)

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:

A superposition of overlapping signals ( $\neq$ isolated event)
Observations of more waveform periods over different time scales ( $\neq$ short-duration signals)
Signal-dominated detection ( $\neq$ noise-dominated)
Reliance on more complex techniques for noise assessment
( $\neq$ regular acquisition of signal-free data)

Space-borne GW Detection: Background

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

https://doi.org/10.1360/SSPMA-2024-0087

Space-borne GW Detection: Background

Analyses cannot treat sources independently and sequentially work through a list of candidate detections.

The data analyses depend on the simultaneous fitting of complete astrophysical, cosmological, and instrument models to the observed data.
The need for this so-called “global fit” was identified as the primary challenge to the data analysis early in the space-borne mission formulation.

(Sec.8.6 The Red Book)

Global Fit

The idea of the global fit method is to comprehensively model all astrophysical and instrumental features present in the space-borne gravitational wave data.

Technical challenges:

High dimensional
Highly correlated
Multimodality
Trans-dimensional

Text

Rapid PE for Space-borne GW Detection

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).

Customization for the Taiji scenario: A scalable approach

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.

A, E(f)=\sum_\alpha \mathcal{T}_\alpha^{A, E}(f) \tilde{h}_\alpha(f), \quad \alpha \in\{+, \times\}

M. Du, B. Liang, HW, P. Xu, Z. Luo, Y. Wu. SCPMA 67, 230412 (2024).

Customization for the Taiji scenario: A scalable approach

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).

Unbiased estimation and confidence validation

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
The remarkable speed of our method, which outpaces traditional techniques by several orders of magnitude, establishes it as an invaluable tool for preprocessing in global fitting.

M. Du, B. Liang, HW, P. Xu, Z. Luo, Y. Wu. SCPMA 67, 230412 (2024).

Multimodality in extrinsic parameters

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.
Impact on PE: The presence of these peaks affects the posterior distributions of extrinsic parameters, potentially leading to inaccuracies in $t_c$ and subsequent parameters due to phase term associations and inherent degeneracies.
Model Performance: Despite the multimodality, the best-fit values from the NF model closely align with true values within the $1\sigma$ range for most parameters, and at least $2\sigma$ for others.
Comparative Analysis: The ML pipeline tends to produce broader posteriors compared to the Bayesian nested sampling approach.

（NF = Normalizing Flow model）

M. Du, B. Liang, HW, P. Xu, Z. Luo, Y. Wu. SCPMA 67, 230412 (2024).

Ongoing & Future Plan

Earth-based GW detection ($\sim10^2$Hz)

A Python Toolbox for Gravitational Wave Astronomy: GWToolkit
- This toolbox is powered by Ray/JAX and supports both CPU and GPU. It is designed specifically for machine learning applications.
Gravitational-Wave Observatory Open Source Data Portal
(基于引力波探测开源数据的共享数据门户)
Can AI identify new GW events from LIGO data?
- Could this be a GW signal beyond General Relativity (GR)?
How can we address the issue of strong or unacceptable biases that occur when outputs from AI models are used jointly or in combination to measure properties of a population, sub-population, or ensemble?
(also addressed by 2405.18095)

Text

Space-based GW detection ($\sim10^{-3}$Hz)

“Global fit” challenge
- How can we achieve and accelerate the Bayesian inference through algorithmic innovations?
  - Flow-based proposal?
  - Transdimensional Nested Sampling?
- How can we leverage powerful LLM-based methods to accomplish this?

Text

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! 🙏')

Ongoing and Future Plan

Earth-based GW detection

A Python Toolbox for Gravitational Wave Astronomy: GWToolkit
- This toolbox is powered by Ray/JAX and supports both CPU and GPU. It is designed specifically for machine learning applications.
Can AI identify new GW events from LIGO data?
- Could this be a GW signal beyond General Relativity (GR)?
How can we address the issue of strong or unacceptable biases that occur when outputs from AI models are used jointly or in combination to measure properties of a population, sub-population, or ensemble?
(also addressed by 2405.18095)

Text

Space-based GW detection

“Global fit” challenge
- How can we achieve and accelerate the Bayesian inference through algorithmic innovations?
  - Flow-based proposal?
  - Transdimensional Nested Sampling?
- How can we leverage powerful LLM-based methods to accomplish this?

Text

This silde: https://slides.com/iphysresearch/2024Aug_ihep

Bouns

Text

Calibration Analysis on Denoised / Residual Data

Denoised data
- Non-Gaussian and non-stationary.
- The SNR is evidently high, but it cannot be utilized for parameter estimation. Because:
  - Background noise is also non-Gaussian.
  - Assessing the unbiasedness of the posterior distribution will be very challenging.
- Is it possible to calibrate or validate the denoised data simultaneously while training the denoiser?
Residual Data (“Global-fit” challenge for LISA)
- Iteratively using exact waveforms is not practical in terms of timeliness, while using approximate waveforms inevitably introduces systematic errors in the residual data during each iteration.
- Is it possible to calibrate the residual data simultaneously during each iteration of the statistical inference?

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).

Key Takeaways & Look Forwards

Ongoing and Future Projects

Neural density estimation

Density fit for posterior distributions
- use the old posterior to form a proposal for the extended data.
Density fit for the Galaxy
- fitt a Galaxy model for joint distribution for $(A, \beta, \lambda)$.
...

Text

Ref:

Ashton, G, and C Talbot. MNRAS 507, no. 2 (2021): 2037–51.
Korsakova, N, et al. (2402.13701)
Wouters, T, et al. (2404.11397)

Ongoing and Future Projects

Neural density estimation

Density fit for posterior distributions
- use the old posterior to form a proposal for the extended data.
Density fit for the Galaxy
- fitt a Galaxy model for joint distribution for $(A, \beta, \lambda)$.
...

Text

nflow

\mathcal{N}(0,\mathbb{I})

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。

Enhancing Gravitational Wave Astronomy with Artificial Intelligence

By He Wang

Enhancing Gravitational Wave Astronomy with Artificial Intelligence

2024年粒子天体物理重点实验室系列学术报告|8月19日上午10:00 @高能所

1,069

He Wang PRO

Knowledge increases by sharing but not by saving.

Enhancing Gravitational Wave Astronomy with Artificial Intelligence

Enhancing Gravitational Wave Astronomy with Artificial Intelligence

Content

Gravitational Wave Astronomy

Gravitational Wave Astronomy

Gravitational Wave Astronomy

Gravitational Wave Astronomy

Technical Challenges: Data Processing for GW

Text

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目录

科学智能：AI for Science

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引力波数据处理：人工智能技术应用

Content

GW search · Pipeline

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GW search · Pipeline

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GW search · Pipeline

GW search · Pipeline

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GW search · Pipeline

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GW search · Pipeline

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GW search · Pipeline

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GW search · Pipeline

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GW search · Pipeline

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GW search · Pipeline

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GW search · Pipeline

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Content

Background

Network Architecture

Data Preprocessing and Training Strategy

Effect on Realistic Noise

Recovery of Binary Black Holes

Recovery of Binary Black Holes

Search Strategy Overview

Search Strategy Overview

Significance Estimates

Text

GW search · Pipeline

Content

Parameter estimation · Scientific discovery

AI for Gravitational Wave: Parameter Estimation

AI for Gravitational Wave: Parameter Estimation

Normalizing Flow Model (1/4)

Normalizing Flow Model (2/4)

Normalizing Flow Model (3/4)

Normalizing Flow Model (4/4)

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Parameter Estimation · Scientific Discovery

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Parameter Estimation · Scientific Discovery

Parameter Estimation · Scientific Discovery

Parameter estimation · Scientific discovery

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Parameter estimation · Scientific discovery

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Content

Key Takeaways

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Key Takeaways

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目录

Space-borne GW Detection: Background