Interpretable Gravitational Wave Data Analysis
with Deep Learning and Large Language Models

He Wang (王赫)

hewang@ucas.ac.cn

Beijing Normal University

International Centre for Theoretical Physics Asia-Pacific (ICTP-AP), UCAS

Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), UCAS

On behave of KAGRA collaboration

The 12th KAGRA international workshop (KIW-12) | May 27, 2025 @SHAO

Gravitational waves (GW) are a strong field effect in General Relativity, ripples in the fabric of spacetime caused by accelerating massive objects.

Gravitational Wave Astronomy

Challenge and Methodology: Detecting Signals in GW Data

GW Data Characteristics

LIGO-VIRGO-KAGRA

LISA Project

Noise: non-Gaussian and non-stationary
Signal challenges:
- (Earth-based) A low signal-to-noise ratio (SNR) which is typically about 1/100 of the noise amplitude (-60 dB).
- (Space-based) A superposition of all GW signals (e.g.: 10⁴ of GBs, 10~10² of SMBHs, and 10~10³ of EMRIs, etc.) received during the mission's observational run.

Matched Filtering Techniques (匹配滤波方法)

In Gaussian and stationary noise environments, the optimal linear algorithm for extracting weak signals
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).

Statistical Approaches

Frequentist Testing:

Make assumptions about signal and noise
Write down the likelihood function
Maximize parameters
Define detection statistic
→ recover MF

Bayesian Testing:

Start from same likelihood
Define parameter priors
Marginalize over parameters
Often treated as Frequentist statistic
→ recover MF (for certain priors)

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Why should you consider applying ML to gravitational wave astrophysics?

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

The "Real" Reasons We Apply ML to GW Astrophysics

Let's be honest about our motivations... 😉

The perfectly valid "scientific" reasons:

✓ It sounded like a cool project
✓ My supervisor said it was a good thing to work on
✓ I will learn some really useful ML skills
✓ I'm already good at ML
✓ I want to get better at ML
✓ I want to get a high-paying job after this PhD/postdoc
✓ I want to be spared when the machines take over

Credit: Chris Messenger (MLA meeting,, Jan 2025)

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Why is AI/ML Everywhere in GW Research?

The core motivations behind nearly all AI+GW research

1

ML is FAST

So much data, so little time!

• Bayesian parameter estimation
• Replaces computationally intensive components

2

ML is ACCURATE*

Consistently outperforms traditional approaches

• Unmodelled burst searches
• Continuous GW searches

3

ML is FLEXIBLE

Provides deeper insights into complex problems

• Reveals patterns through interpretability
• Enables previously impractical approaches

* When properly trained and validated on appropriate datasets

Credit: Chris Messenger (MLA meeting,, Jan 2025)

Key question: If an ML (or any) analysis doesn't do 1 or more of these things, then from a scientific perspective,
what is the point?

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Is It Really So Simple?

The reality of ML in scientific research is more nuanced

No: We need to think more critically

❓ Are we just trying to predict a function?
❓ Are there any astrophysical constraints?
❓ Do we need to understand how/why it works?
❓ What about errors? Quality flags?
❓ What happens if things go wrong?

Twitter: @DeepLearningAI_

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Why Even Use AI?

The mathematical inevitability and the path to understanding

Universal Approximation Theorem

The existence theorem that guarantees solutions

Neural networks with sufficient hidden layers can approximate any continuous function on compact subsets of \(\mathbb{R}^n\)
Ref: Cybenko, G. (1989), Hornik et al. (1989)

The solution is mathematically guaranteed — our challenge is finding the path to it

1

Machine learning will win in the long run

AI models still have vast potential compared to the human brain's efficiency. Beating traditional methods is mathematically inevitable given sufficient resources.

2

The question is not if AI/ML will win, but how

Understanding AI's inner workings is the real challenge, not proving its capabilities.

That's where we can learn something exciting with Foundation Models.

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Core Insight from Computer Vision

Direct approach from Computer Vision (CV) to GW signal processing: pixel point \(\Rightarrow\) sampling point.
The CNN framework treats time series data similar to images, where each sampling point represents a feature to learn.

Performance Analysis

Convolutional neural networks (CNN) can achieve comparable performance to Matched Filtering under Gaussian stationary noise.
CNNs significantly outperform traditional methods in terms of execution speed (with GPU support).
Modern architectures show improved robustness against non-Gaussian noise transients (glitches).

Pioneering Research Publications

PRL, 2018, 120(14): 141103.

PRD, 2018, 97(4): 044039.

He Wang | ICTP-AP, UCAS

CNN for GW Detection: Pioneering Approaches

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Matched-filtering Convolutional Neural Network (MFCNN)

HW, SC Wu, ZJ CAO, et al. PRD 101, 10 (2020): 104003

He Wang | ICTP-AP, UCAS

CNN for GW Detection: Feature Extraction

Convolutional Neural Network (ConvNet or CNN)

feature extraction

classifier

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

>> Is it matched-filtering ?
>> Wait, It can be matched-filtering!

Matched-filtering (cross-correlation with templates) can be interpreted as a convolutional layer with predefined kernels.

GW150914

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Universal Approximation Theorem: Existence Theorem

Neural networks with sufficient hidden layers can approximate any continuous function on compact subsets of \(\mathbb{R}^n\).
For GW detection, this means CNNs can theoretically learn the optimal detection statistics without explicit physical modeling.
The expressive power of deep neural networks enables capturing complex patterns in non-Gaussian, non-stationary noise.
Ref: Cybenko, G. (1989), Hornik et al. (1989)

Beyond Speed: Generalization and Explainability

Improving AI explainability reveals deep connections between CNN architectures and matched filtering techniques.
Matched-filtering (cross-correlation with templates) can be interpreted as a convolutional layer with predefined kernels.
In practice, we use matched filters as an essential component of feature extraction in CNNs for GW detection.

Convolutional Neural Network (ConvNet or CNN)

Matched-filtering Convolutional Neural Network (MFCNN)

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

He Wang | ICTP-AP, UCAS

CNN for GW Detection: Feature Extraction

GW150914

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

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

\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 \(d(t)\)

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

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

Time Domain

(matched-filtering)

(normalizing)

(whitening)

\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

Frequency Domain

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

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

CNN for GW Detection: Feature Extraction

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

CNN for GW Detection: Feature Extraction

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

\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 \(d(t)\)

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)

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

Time Domain

(matched-filtering)

(normalizing)

(whitening)

\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

Frequency Domain

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

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

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

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

1 sec duration

35 templates used

Explainable AI Approach

Implements matched filtering operations through custom convolutional layers
Makes the network more interpretable by embedding domain knowledge
Connects traditional signal processing with deep learning
Outperforms standard CNNs in both accuracy and efficiency

Matched-filtering Convolutional Neural Network (MFCNN)

The available codes (2019): https://gist.github.com/iphysresearch/a00009c1eede565090dbd29b18ae982c

CNN for GW Detection: Feature Extraction

HW, SC Wu, ZJ CAO, et al. PRD 101, 10 (2020): 104003

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Gravitational wave signal search algorithm benchmark (MLGWSC-1)
Dataset-4: Sampled from O3a real gravitational wave observation data

First Benchmark for GW Detection Algorithms

Benchmark Results

Publications

Key Findings

On simulated noise data, machine learning algorithms are highly competitive compared to LIGO's most sensitive signal search pipelines
Most tested machine learning algorithms are overly sensitive to non-Gaussian real noise backgrounds, resulting in high false alarm rates

Traditional signal search algorithms can identify gravitational wave signals at low false alarm rates with assured confidence
Tested machine learning algorithms have very limited ability to identify long-duration signals

Note on Benchmark Limitations:

Outperforming PyCBC doesn't conclusively prove that matched filtering is inferior to AI methods. This is both because the dataset represents a specific distribution and because PyCBC settings could be further optimized for this particular benchmark.

He Wang | ICTP-AP, UCAS

arXiv:2501.13846 [gr-qc]

Phys. Rev. D 110, 024024 (2024)

Phys. Rev. D 107, 023021 (2023)

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Interpretability Challenges: Comparing Detection Statistics

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.

AI Model Denoising

Our Model's Detection Statistics

LVK Official Detection Statistics

Signal denoising visualization using our deep learning model (Transformer-based)

Detection statistics from our AI model showing O1 events

HW et al 2024 MLST 5 015046

GW151226

GW151012

Official detection statistics from LVK collaboration

LVK. PRD (2016). arXiv:1602.03839

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

arXiv:2407.07820 [gr-qc]

Recent AI Discoveries & Validation Hurdles:

A recent study (arXiv:2407.07820) demonstrates how a ResNet-based (CNN) architecture with careful signal search strategy and post-processing can identify 8 new potential gravitational wave events from LIGO O3 data.
The absence of these events in traditional PyCBC results raises questions: could adjustments to rate priors and p_astro parameters in signal models help traditional pipelines detect these candidates (if they are real GW events)?
The ideal approach combines multiple diverse pipelines working in parallel to ensure comprehensive detection (requiring interpretable models) and using evidence-based detection statistics while simultaneously optimizing both real signal population (p_astro) and noise model (likelihood) fits.

Search

PE

Rate

Key Insight:

Interpretability Challenges: Discoveries vs. Validation (part 1/2)

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Interpretability Challenges: Discoveries vs. Validation (part 1/2)

Recent AI Discoveries & Validation Hurdles:

A recent study (arXiv:2407.07820) demonstrates how a ResNet-based (CNN) architecture with careful signal search strategy and post-processing can identify 8 new potential gravitational wave events from LIGO O3 data.
The absence of these events in traditional PyCBC results raises questions: could adjustments to rate priors and p_astro parameters in signal models help traditional pipelines detect these candidates (if they are real GW events)?
The ideal approach combines multiple diverse pipelines working in parallel to ensure comprehensive detection (requiring interpretable models) and using evidence-based detection statistics while simultaneously optimizing both real signal population (p_astro) and noise model (likelihood) fits.

Search

PE

Rate

Key Insight:

Credit: DCC-XXXXXXXX

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Parameter Estimation Challenges with AI Models:

In parameter estimation, AI models' lack of interpretability requires substantial additional scientific validation to ensure credibility and acceptance of results.
Parameter distributions from AI models often lack robustness across different noise realizations and are difficult to calibrate against established methods.
Scientific papers using AI methods must dedicate significant space to validation procedures, comparing against traditional methods and demonstrating reliability across multiple test cases.

arXiv:2404.14286

Phys. Rev. D 109, 123547 (2024)

Interpretability Challenges: Discoveries vs. Validation (part 2/2)

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Representation Space Interpolation

Core Insights: Generative models' ability to perform accurate statistical inference can be understood as manifold learning rather than mere density estimation:

Models learn a continuous latent manifold of data distributions
Statistical parameters act as coordinates in this space
Inference occurs through latent space navigation
Enables robust generalization for complex distributions

He Wang | ICTP-AP, UCAS

Theoretical Understanding of Generative Models

Generative models don't memorize examples, but learn a continuous manifold
where similar concepts lie near each other. Statistical inference becomes
a form of navigation through this learned representation space.

Deep Learning is Not As Impressive As you Think, It's Mere Interpolation.

CVAE

Encodes data into latent space, enabling conditional generation

Flow-based

Transforms simple distributions into complex ones via invertible mappings

Interpretable Gravitational Wave Data Analysis with DL and LLMs

AI for Science

The core driving force of AI4Sci largely lies in its “interpolation” generalization capabilities, showcasing its powerful complex modeling abilities.

From 李宏毅

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

The core driving force of AI4Sci largely lies in its “interpolation” generalization capabilities, showcasing its powerful complex modeling abilities.

AI for Science

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Test of General Relatively

2403.18936

He Wang | ICTP-AP, UCAS

Why Do We Trust Traditional Models?

Key Trust Factors:

✓ Interpretable: Parameters have physical meaning
✓ Built-in uncertainties: Input uncertainties propagate to outputs
✓ Model selection: Balance simplicity with accuracy
✓ Scientific insight: Reduces complexity, reveals principles

Traditional Physics Approach

Input

Human-Designed Algorithm

(Based on human insight)

Output

Example: Matched Filtering,
Linear Regression

Black-Box AI Approach

Input

AI Model

(Low interpretability)

Output

Examples: CNN, AlphaGo, DINGO

Key Challenge: How can we maintain the interpretability advantages of traditional models while leveraging the power of AI approaches?

Data/
Experience

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Bridging Trust & Performance in GW Analysis

Combining the interpretability of physics with the power of AI

Our Mission: To create transparent AI systems that combine physics-based interpretability with deep learning capabilities

Interpretable AI Approach

The best of both worlds

Input

Physics-Informed
Algorithm

(High interpretability)

Output

Example: Our Approach
(In Preparation)

AI Model

Physics
Knowledge

Traditional Physics Approach

Input

Human-Designed Algorithm

(Based on human insight)

Output

Example: Matched Filtering, linear regression

Black-Box AI Approach

Input

AI Model

(Low interpretability)

Output

Examples: CNN, AlphaGo, DINGO

Data/
Experience

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Automated Heuristic Design: Problem Definition

He Wang | ICTP-AP, UCAS

For any complex task \(P\) (especially NP-hard problems), Automated Heuristic Design (AHD) searches for the optimal heuristic \(h^*\) within a heuristic space \(H\):

\(h^*=\underset{h \in H}{\arg \max } g(h) \)

The heuristic space \(H\) contains all feasible algorithmic solutions for task \(P\). Each heuristic \(h \in H\) maps from the set of task inputs \(I_P\) to corresponding solutions \(S_P\):

\(h: I_P \rightarrow S_P\)

Performance measure \(g(\cdot)\) evaluates each heuristic's effectiveness, \(g: H \rightarrow \mathbb{R}\). For minimization problems with objective function \(f: S_P \rightarrow \mathbb{R}\), we estimate performance by evaluating the heuristic instances \({ins}\in D \subseteq I_P\) on dataset \(D\) as follows:

\(g(h)=\mathbb{E}_{\boldsymbol{ins} \in D}[-f(h(\boldsymbol{ins}))]\)

arXiv.2410.14716

P

H

S_p

\mathbb{R}

f

I_p

h

external_knowledge
(constraint)

h

g(h)

Interpretable Gravitational Wave Data Analysis with DL and LLMs

HW et al., In preparation

He Wang | ICTP-AP, UCAS

import numpy as np
import scipy.signal as signal
def pipeline_v1(strain_h1: np.ndarray, strain_l1: np.ndarray, times: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    def data_conditioning(strain_h1: np.ndarray, strain_l1: np.ndarray, times: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        window_length = 4096
        dt = times[1] - times[0]
        fs = 1.0 / dt
        
        def whiten_strain(strain):
            strain_zeromean = strain - np.mean(strain)
            freqs, psd = signal.welch(strain_zeromean, fs=fs, nperseg=window_length,
                                       window='hann', noverlap=window_length//2)
            smoothed_psd = np.convolve(psd, np.ones(32) / 32, mode='same')
            smoothed_psd = np.maximum(smoothed_psd, np.finfo(float).tiny)
            white_fft = np.fft.rfft(strain_zeromean) / np.sqrt(np.interp(np.fft.rfftfreq(len(strain_zeromean), d=dt), freqs, smoothed_psd))
            return np.fft.irfft(white_fft)

        whitened_h1 = whiten_strain(strain_h1)
        whitened_l1 = whiten_strain(strain_l1)
        
        return whitened_h1, whitened_l1, times
    
    def compute_metric_series(h1_data: np.ndarray, l1_data: np.ndarray, time_series: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
        fs = 1 / (time_series[1] - time_series[0])
        f_h1, t_h1, Sxx_h1 = signal.spectrogram(h1_data, fs=fs, nperseg=256, noverlap=128, mode='magnitude', detrend=False)
        f_l1, t_l1, Sxx_l1 = signal.spectrogram(l1_data, fs=fs, nperseg=256, noverlap=128, mode='magnitude', detrend=False)
        tf_metric = np.mean((Sxx_h1**2 + Sxx_l1**2) / 2, axis=0)
        gps_mid_time = time_series[0] + (time_series[-1] - time_series[0]) / 2
        metric_times = gps_mid_time + (t_h1 - t_h1[-1] / 2)
        
        return tf_metric, metric_times

    def calculate_statistics(tf_metric, t_h1):
        background_level = np.median(tf_metric)
        peaks, _ = signal.find_peaks(tf_metric, height=background_level * 1.0, distance=2, prominence=background_level * 0.3)
        peak_times = t_h1[peaks]
        peak_heights = tf_metric[peaks]
        peak_deltat = np.full(len(peak_times), 10.0)  # Fixed uncertainty value
        return peak_times, peak_heights, peak_deltat

    whitened_h1, whitened_l1, data_times = data_conditioning(strain_h1, strain_l1, times)
    tf_metric, metric_times = compute_metric_series(whitened_h1, whitened_l1, data_times)
    peak_times, peak_heights, peak_deltat = calculate_statistics(tf_metric, metric_times)
    
    return peak_times, peak_heights, peak_deltat

Input: H1 and L1 detector strains, time array | Output: Event times, significance values, and time uncertainties

P

H

S_p

\mathbb{R}

f

I_p

h

external_knowledge
(constraint)

h

g(h)

Problem: Pipeline Workflow

Conditions raw detector data (whitening)
Computes time-frequency metrics
Identifies peaks above background
Returns event candidates with timestamps

Optimization Target: Maximizing Area Under Curve (AUC) in the 1-1000Hz false alarms per-year range, balancing detection sensitivity and false alarm rates across algorithm generations

Automated Heuristic Design: Problem Definition

Interpretable Gravitational Wave Data Analysis with DL and LLMs

HW et al., In preparation

He Wang | ICTP-AP, UCAS

Algorithmic Exploration：LLM Prompt Engineering

external_knowledge
(constraint)

h

g(h)

Prompt Structure for Algorithm Evolution

This template guides the LLM to generate optimized gravitational wave detection algorithms by learning from comparative examples.

Key Components:

Expert role establishment
Example pair analysis (worse/better algorithm)
Reflection on improvements
Targeted new algorithm generation
Strict output format enforcement

You are an expert in gravitational wave signal detection algorithms. Your task is to design heuristics that can effectively solve optimization problems.

{prompt_task}

I have analyzed two algorithms and provided a reflection on their differences. 

[Worse code]
{worse_code}

[Better code]
{better_code}

[Reflection]
{reflection}

Based on this reflection, please write an improved algorithm according to the reflection. 
First, describe the design idea and main steps of your algorithm in one sentence. The description must be inside a brace outside the code implementation. Next, implement it in Python as a function named '{func_name}'.
This function should accept {input_count} input(s): {joined_inputs}. The function should return {output_count} output(s): {joined_outputs}. 
{inout_inf} {other_inf}

Do not give additional explanations.

One Prompt Template for MLGWSC1 Algorithm Synthesis

Interpretable Gravitational Wave Data Analysis with DL and LLMs

HW et al., In preparation

Algorithmic Synergy: MCTS, Evolution & LLM Agents

Monte Carlo Tree Search (MCTS)

Efficiently explores high-dimensional spaces
Balances exploration and exploitation
Provides strong theoretical guarantees
Excels in complex sequential decision-making

Evolutionary Algorithms

Enables gradient-free global optimization
Naturally handles multi-objective problems
Provides diverse solution candidates
Robust to noisy objective functions

LLM Agents

Processes and generates domain-specific text
Understands and generates code
Reasons through complex problem spaces
Adapts strategies based on context

He Wang | ICTP-AP, UCAS

Together, these approaches create a powerful framework for heuristic optimization of gravitational wave signal search algorithms

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Algorithmic Synergy: MCTS, Evolution & LLM Agents

He Wang | ICTP-AP, UCAS

Proposed framework integrating MCTS decision-making, self-evolutionary optimization, and LLM agent guidance for gravitational wave signal search

With route/short/long-term reflection：《Thinking, Fast and Slow》

deepseek-R1 for reflection generation
o3-mini-medium for code generation

Preliminary Results (February 2025)

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

MLGWSC1 preliminary 结果

MLGWSC1: Algorithm Evolutionary Tree Visualization

Tree-based representation of our framework's exploration path, where each node represents a unique algorithm variant generated during the optimization process

Node color intensity: Algorithm performance level | Connections: Algorithmic modifications | Tree depth: Iteration sequence

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Preliminary Results (February 2025)

MLGWSC1 Benchmark: Optimization Performance Results

Optimization Progress & Algorithm Diversity

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

HW et al., In preparation

Pipeline Workflow

Conditions raw detector data (whitening)
Computes time-frequency metrics
Identifies peaks above background
Returns event candidates with timestamps

Diversity in Evolutionary Computation

Population encoding:

Removing comments and docstrings using abstract-syntax tree,
standardizing code snippets into a common coding style (e.g., PEP81),
Convert code snippets to vector representations using a code embedding model.

Pipeline Workflow

Conditions raw detector data (whitening)
Computes time-frequency metrics
Identifies peaks above background
Returns event candidates with timestamps

MLGWSC1 Benchmark: Optimization Performance Results

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

HW et al., In preparation

Refs of Benchmark Models

Sage: 2501.13846 [gr-qc]
Virgo-AUTh / TPI FSU Jena: PRD 110, 024024 (2024)
Others: PRD 107, 023021 (2023)

Interpretability Analysis: PT Level 5

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

HW et al., In preparation

import numpy as np
import scipy.signal as signal
from scipy.signal.windows import tukey
from scipy.signal import savgol_filter

def pipeline_v2(strain_h1: np.ndarray, strain_l1: np.ndarray, times: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    The pipeline function processes gravitational wave data from the H1 and L1 detectors to identify potential gravitational wave signals.
    It takes strain_h1 and strain_l1 numpy arrays containing detector data, and times array with corresponding time points.
    The function returns a tuple of three numpy arrays: peak_times containing GPS times of identified events,
    peak_heights with significance values of each peak, and peak_deltat showing time window uncertainty for each peak.
    """
    eps = np.finfo(float).tiny
    dt = times[1] - times[0]
    fs = 1.0 / dt
    # Base spectrogram parameters
    base_nperseg = 256
    base_noverlap = base_nperseg // 2
    medfilt_kernel = 101       # odd kernel size for robust detrending
    uncertainty_window = 5     # half-window for local timing uncertainty

    # -------------------- Stage 1: Robust Baseline Detrending --------------------
    # Remove long-term trends using a median filter for each channel.
    detrended_h1 = strain_h1 - signal.medfilt(strain_h1, kernel_size=medfilt_kernel)
    detrended_l1 = strain_l1 - signal.medfilt(strain_l1, kernel_size=medfilt_kernel)

    # -------------------- Stage 2: Adaptive Whitening with Enhanced PSD Smoothing --------------------
    def adaptive_whitening(strain: np.ndarray) -> np.ndarray:
        # Center the signal.
        centered = strain - np.mean(strain)
        n_samples = len(centered)
        # Adaptive window length: between 5 and 30 seconds
        win_length_sec = np.clip(n_samples / fs / 20, 5, 30)
        nperseg_adapt = int(win_length_sec * fs)
        nperseg_adapt = max(10, min(nperseg_adapt, n_samples))
        
        # Create a Tukey window with 75% overlap.
        tukey_alpha = 0.25
        win = tukey(nperseg_adapt, alpha=tukey_alpha)
        noverlap_adapt = int(nperseg_adapt * 0.75)
        if noverlap_adapt >= nperseg_adapt:
            noverlap_adapt = nperseg_adapt - 1
        
        # Estimate the power spectral density (PSD) using Welch's method.
        freqs, psd = signal.welch(centered, fs=fs, nperseg=nperseg_adapt,
                                  noverlap=noverlap_adapt, window=win, detrend='constant')
        psd = np.maximum(psd, eps)
        
        # Compute relative differences for PSD stationarity measure.
        diff_arr = np.abs(np.diff(psd)) / (psd[:-1] + eps)
        # Smooth the derivative with a moving average.
        if len(diff_arr) >= 3:
            smooth_diff = np.convolve(diff_arr, np.ones(3)/3, mode='same')
        else:
            smooth_diff = diff_arr
        
        # Exponential smoothing (Kalman-like) with adaptive alpha using PSD stationarity.
        smoothed_psd = np.copy(psd)
        for i in range(1, len(psd)):
            # Adaptive smoothing coefficient: base 0.8 modified by local stationarity (±0.05)
            local_alpha = np.clip(0.8 - 0.05 * smooth_diff[min(i-1, len(smooth_diff)-1)], 0.75, 0.85)
            smoothed_psd[i] = local_alpha * smoothed_psd[i-1] + (1 - local_alpha) * psd[i]
            
        # Compute Tikhonov regularization gain based on deviation from median PSD.
        noise_baseline = np.median(smoothed_psd)
        raw_gain = (smoothed_psd / (noise_baseline + eps)) - 1.0
        
        # Compute a causal-like gradient using the Savitzky-Golay filter.
        win_len = 11 if len(smoothed_psd) >= 11 else ((len(smoothed_psd)//2)*2+1)
        polyorder = 2 if win_len > 2 else 1
        delta_freq = np.mean(np.diff(freqs))
        grad_psd = savgol_filter(smoothed_psd, win_len, polyorder, deriv=1, delta=delta_freq, mode='interp')
        
        # Nonlinear scaling via sigmoid to enhance gradient differences.
        sigmoid = lambda x: 1.0 / (1.0 + np.exp(-x))
        scaling_factor = 1.0 + 2.0 * sigmoid(np.abs(grad_psd) / (np.median(smoothed_psd) + eps))
        
        # Compute adaptive gain factors with nonlinear scaling.
        gain = 1.0 - np.exp(-0.5 * scaling_factor * raw_gain)
        gain = np.clip(gain, -8.0, 8.0)
        
        # FFT-based whitening: interpolate gain and PSD onto FFT frequency bins.
        signal_fft = np.fft.rfft(centered)
        freq_bins = np.fft.rfftfreq(n_samples, d=dt)
        interp_gain = np.interp(freq_bins, freqs, gain, left=gain[0], right=gain[-1])
        interp_psd = np.interp(freq_bins, freqs, smoothed_psd, left=smoothed_psd[0], right=smoothed_psd[-1])
        denom = np.sqrt(interp_psd) * (np.abs(interp_gain) + eps)
        denom = np.maximum(denom, eps)
        white_fft = signal_fft / denom
        whitened = np.fft.irfft(white_fft, n=n_samples)
        return whitened

    # Whiten H1 and L1 channels using the adapted method.
    white_h1 = adaptive_whitening(detrended_h1)
    white_l1 = adaptive_whitening(detrended_l1)

    # -------------------- Stage 3: Coherent Time-Frequency Metric with Frequency-Conditioned Regularization --------------------
    def compute_coherent_metric(w1: np.ndarray, w2: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
        # Compute complex spectrograms preserving phase information.
        f1, t_spec, Sxx1 = signal.spectrogram(w1, fs=fs, nperseg=base_nperseg,
                                              noverlap=base_noverlap, mode='complex', detrend=False)
        f2, t_spec2, Sxx2 = signal.spectrogram(w2, fs=fs, nperseg=base_nperseg,
                                               noverlap=base_noverlap, mode='complex', detrend=False)
        # Ensure common time axis length.
        common_len = min(len(t_spec), len(t_spec2))
        t_spec = t_spec[:common_len]
        Sxx1 = Sxx1[:, :common_len]
        Sxx2 = Sxx2[:, :common_len]
        
        # Compute phase differences and coherence between detectors.
        phase_diff = np.angle(Sxx1) - np.angle(Sxx2)
        phase_coherence = np.abs(np.cos(phase_diff))
        
        # Estimate median PSD per frequency bin from the spectrograms.
        psd1 = np.median(np.abs(Sxx1)**2, axis=1)
        psd2 = np.median(np.abs(Sxx2)**2, axis=1)
        
        # Frequency-conditioned regularization gain (reflection-guided).
        lambda_f = 0.5 * ((np.median(psd1) / (psd1 + eps)) + (np.median(psd2) / (psd2 + eps)))
        lambda_f = np.clip(lambda_f, 1e-4, 1e-2)
        # Regularization denominator integrating detector PSDs and lambda.
        reg_denom = (psd1[:, None] + psd2[:, None] + lambda_f[:, None] + eps)
        
        # Weighted phase coherence that balances phase alignment with noise levels.
        weighted_comp = phase_coherence / reg_denom
        
        # Compute axial (frequency) second derivatives as curvature estimates.
        d2_coh = np.gradient(np.gradient(phase_coherence, axis=0), axis=0)
        avg_curvature = np.mean(np.abs(d2_coh), axis=0)
        
        # Nonlinear activation boost using tanh for regions of high curvature.
        nonlinear_boost = np.tanh(5 * avg_curvature)
        linear_boost = 1.0 + 0.1 * avg_curvature
        
        # Cross-detector synergy: weight derived from global median consistency.
        novel_weight = np.mean((np.median(psd1) + np.median(psd2)) / (psd1[:, None] + psd2[:, None] + eps), axis=0)
        
        # Integrated time-frequency metric combining all enhancements.
        tf_metric = np.sum(weighted_comp * linear_boost * (1.0 + nonlinear_boost), axis=0) * novel_weight
        
        # Adjust the spectrogram time axis to account for window delay.
        metric_times = t_spec + times[0] + (base_nperseg / 2) / fs
        return tf_metric, metric_times

    tf_metric, metric_times = compute_coherent_metric(white_h1, white_l1)

    # -------------------- Stage 4: Multi-Resolution Thresholding with Octave-Spaced Dyadic Wavelet Validation --------------------
    def multi_resolution_thresholding(metric: np.ndarray, times_arr: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        # Robust background estimation with median and MAD.
        bg_level = np.median(metric)
        mad_val = np.median(np.abs(metric - bg_level))
        robust_std = 1.4826 * mad_val
        threshold = bg_level + 1.5 * robust_std

        # Identify candidate peaks using prominence and minimum distance criteria.
        peaks, _ = signal.find_peaks(metric, height=threshold, distance=2, prominence=0.8 * robust_std)
        if peaks.size == 0:
            return np.array([]), np.array([]), np.array([])

        # Local uncertainty estimation using a Gaussian-weighted convolution.
        win_range = np.arange(-uncertainty_window, uncertainty_window + 1)
        sigma = uncertainty_window / 2.5
        gauss_kernel = np.exp(-0.5 * (win_range / sigma) ** 2)
        gauss_kernel /= np.sum(gauss_kernel)
        weighted_mean = np.convolve(metric, gauss_kernel, mode='same')
        weighted_sq = np.convolve(metric ** 2, gauss_kernel, mode='same')
        variances = np.maximum(weighted_sq - weighted_mean ** 2, 0.0)
        uncertainties = np.sqrt(variances)
        uncertainties = np.maximum(uncertainties, 0.01)

        valid_times = []
        valid_heights = []
        valid_uncerts = []
        n_metric = len(metric)

        # Compute a simple second derivative for local curvature checking.
        if n_metric > 2:
            second_deriv = np.diff(metric, n=2)
            second_deriv = np.pad(second_deriv, (1, 1), mode='edge')
        else:
            second_deriv = np.zeros_like(metric)

        # Use octave-spaced scales (dyadic wavelet validation) to validate peak significance.
        widths = np.arange(1, 9)  # approximate scales 1 to 8
        for peak in peaks:
            # Skip peaks lacking sufficient negative curvature.
            if second_deriv[peak] > -0.1 * robust_std:
                continue
            local_start = max(0, peak - uncertainty_window)
            local_end = min(n_metric, peak + uncertainty_window + 1)
            local_segment = metric[local_start:local_end]
            if len(local_segment) < 3:
                continue
            try:
                cwt_coeff = signal.cwt(local_segment, signal.ricker, widths)
            except Exception:
                continue
            max_coeff = np.max(np.abs(cwt_coeff))
            # Threshold for validating the candidate using local MAD.
            cwt_thresh = mad_val * np.sqrt(2 * np.log(len(local_segment) + eps))
            if max_coeff >= cwt_thresh:
                valid_times.append(times_arr[peak])
                valid_heights.append(metric[peak])
                valid_uncerts.append(uncertainties[peak])

        if len(valid_times) == 0:
            return np.array([]), np.array([]), np.array([])
        return np.array(valid_times), np.array(valid_heights), np.array(valid_uncerts)

    peak_times, peak_heights, peak_deltat = multi_resolution_thresholding(tf_metric, metric_times)
    return peak_times, peak_heights, peak_deltat

Interpretability Analysis: Generalization

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

HW et al., In preparation

Out-of-distribution (OOD) detection

The process of identifying data points (1 day) that are not representative of the data distribution the model was trained on (7 days).

Explainable Robustness

Assess the top 30 algorithms on the 1-day test dataset, fine-tuning trigger uncertainty for robust with a certain level of interpretability.

Framework Mechanism Analysis

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

HW et al., In preparation

Effect of scale

Ablation study of various LLM contributions (code generator) and their robustness (Each curve is averaged over at least 5 runs)

Contributions of knowledge synthesis

Compare to w/o external knowledge (linear only; no content used)

He Wang | ICTP-AP, UCAS

Bridging Trust & Performance in GW Analysis

Combining the interpretability of physics with the power of AI

Our Mission: To create transparent AI systems that combine physics-based interpretability with deep learning capabilities

Interpretable AI Approach

The best of both worlds

Input

Physics-Informed
AI Algorithm

(High interpretability)

Output

Example: Our Approach
(In Preparation)

AI Model

Physics
Knowledge

Traditional Physics Approach

Input

Human-Designed Algorithm

(Based on human insight)

Output

Example: Matched Filtering, linear regression

Black-Box AI Approach

Input

AI Model

(Low interpretability)

Output

Examples: CNN, AlphaGo, DINGO

Data/
Experience

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Summary: AI for Gravitational Wave Science

Key Insights from Our Journey

Deep learning methods have transformed GW data analysis, enabling detection capabilities that complement traditional approaches
Evolution from simple CNN architectures to sophisticated frameworks that leverage domain knowledge
LLM-guided algorithmic optimization demonstrates potential for creating high-performance, interpretable methods
Balancing sensitivity and false alarm rates remains a key challenge
Benchmark results validate the potential of AI-driven approaches in scientific discovery

The Critical Role of Interpretability

Algorithm interpretability provides multiple essential benefits:

Scientific Understanding: Reveals unique characteristics of different model architectures and their decision processes
Algorithm Interpolation: Enables meaningful combination of different approaches by understanding their complementary strengths
Result Controllability: Provides confidence in outcomes and minimizes unexplained behaviors
Model Calibration: Allows fine-tuning of algorithms based on physical understanding rather than black-box optimization

The future of gravitational wave science lies at the intersection of traditional physics-inspired methods and interpretable AI approaches, creating a new paradigm for reliable scientific discovery.

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Summary: AI for Gravitational Wave Science

Key Insights from Our Journey

Deep learning methods have transformed GW data analysis, enabling detection capabilities that complement traditional approaches
Evolution from simple CNN architectures to sophisticated frameworks that leverage domain knowledge
LLM-guided algorithmic optimization demonstrates potential for creating high-performance, interpretable methods
Balancing sensitivity and false alarm rates remains a key challenge
Benchmark results validate the potential of AI-driven approaches in scientific discovery

The Critical Role of Interpretability

Algorithm interpretability provides multiple essential benefits:

Scientific Understanding: Reveals unique characteristics of different model architectures and their decision processes
Algorithm Interpolation: Enables meaningful combination of different approaches by understanding their complementary strengths
Result Controllability: Provides confidence in outcomes and minimizes unexplained behaviors
Model Calibration: Allows fine-tuning of algorithms based on physical understanding rather than black-box optimization

The future of gravitational wave science lies at the intersection of traditional physics-inspired methods and interpretable AI approaches, creating a new paradigm for reliable scientific discovery.

for _ in range(num_of_audiences):
    print('Thank you for your attention! 🙏')

hewang@ucas.ac.cn

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Key Questions

Q1: Can LLMs truly generate novel content beyond their training data?

Q2: Why can LLMs perform reasoning in ways that remain imperceptible to us?

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

The Rise of LLMs: How Code Training Transformed AI Capabilities

Evolution of GPT Capabilities

A careful examination of GPT-3.5's capabilities reveals the origins of its emergent abilities:

Original GPT-3 gained generative abilities, world knowledge, and in-context learning through pretraining
Instruction-tuned models developed the ability to follow directions and generalize to unseen tasks
Code-trained models (code-davinci-002) acquired code comprehension
The ability to perform complex reasoning likely emerged as a byproduct of code training

GPT-3.5 series [Source: University of Edinburgh, Allen Institute for AI]

He Wang | ICTP-AP, UCAS

GPT-3 (2020)

ChatGPT (2022)

Magic: Code + Text

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Recent research demonstrates that LLMs can solve complex optimization problems through carefully engineered prompts. DeepMind's OPRO (Optimization by PROmpting) approach showcases how LLMs can generate increasingly refined solutions through iterative prompting techniques.

OPRO: Optimization by PROmpting

Example: Least squares optimization through prompt engineering

arXiv:2309.03409 [cs.NE]

Two Directions of LLM-based Optimization

arXiv:2405.10098 [cs.NE]

He Wang | ICTP-AP, UCAS

The Optimization Potential of Large Language Models

LLMs can generate high-quality solutions to optimization problems without specialized training

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Theoretical Understanding of LLMs' Emergent Abilities

The Interpolation Theory

LLMs' ability to generate novel responses from few examples is increasingly understood as manifold interpolation rather than mere memorization:

LLMs learn a continuous semantic manifold of language during pre-training
Few-shot examples serve as anchor points in this high-dimensional space
The model interpolates between examples to generate responses for novel inputs
This enables coherent generalization beyond the training distribution
The quality of interpolation improves with model scale and training data breadth

The theory suggests that in-context learning is not "learning" in the traditional sense, but rather a form of implicit conditioning on the manifold of learned representations.

Representation Space Interpolation

Real-world Case: FunSearch (Nature, 2023)

Google DeepMind's FunSearch system pairs LLMs with evaluators in an evolutionary process
Discovered new mathematical knowledge for the cap set problem in combinatorics, improving on best known bounds
Also created novel algorithms for online bin packing that outperform traditional methods
Demonstrates LLMs can make verifiable scientific discoveries beyond their training data

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Key Questions

Q1: Can LLMs truly generate novel content beyond their training data?

Q2: Why can LLMs perform reasoning in ways that remain imperceptible to us?

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Iterative Inference: The New Frontier of LLM Scaling

He Wang | ICTP-AP, UCAS

📄 Google DeepMind: "Scaling LLM Test-Time Compute Optimally" (arXiv:2408.03314)

🔗 OpenAI: Learning to Reason with LLMs

Iterative refinement during inference dramatically improves reasoning capabilities without increasing model size or retraining

Performance improvements with test-time compute scaling

From pre-training to test-time:
Three scaling regimes

Different search methods for iterative reasoning

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Bridging Trust & Performance in GW Analysis

Combining the interpretability of physics with the power of AI

Our Mission: To create transparent AI systems that combine physics-based interpretability with deep learning capabilities

Interpretable AI Approach

The best of both worlds

Input

Physics-Informed
Algorithm

(High interpretability)

Output

Example: Our Approach
(In Preparation)

AI Model

Physics
Knowledge

Traditional Physics Approach

Input

Human-Designed Algorithm

(Based on human insight)

Output

Example: Matched Filtering, linear regression

Black-Box AI Approach

Input

AI Model

(Low interpretability)

Output

Examples: CNN, AlphaGo, DINGO

Data/
Experience

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Summary: AI for Gravitational Wave Science

Key Insights from Our Journey

Deep learning methods have transformed GW data analysis, enabling detection capabilities that complement traditional approaches
Evolution from simple CNN architectures to sophisticated frameworks that leverage domain knowledge
LLM-guided algorithmic optimization demonstrates potential for creating high-performance, interpretable methods
Balancing sensitivity and false alarm rates remains a key challenge
Benchmark results validate the potential of AI-driven approaches in scientific discovery

The Critical Role of Interpretability

Algorithm interpretability provides multiple essential benefits:

Scientific Understanding: Reveals unique characteristics of different model architectures and their decision processes
Algorithm Interpolation: Enables meaningful combination of different approaches by understanding their complementary strengths
Result Controllability: Provides confidence in outcomes and minimizes unexplained behaviors
Model Calibration: Allows fine-tuning of algorithms based on physical understanding rather than black-box optimization

The future of gravitational wave science lies at the intersection of traditional physics-inspired methods and interpretable AI approaches, creating a new paradigm for reliable scientific discovery.

He Wang | ICTP-AP, UCAS

Interpretable Gravitational Wave Data Analysis with DL and LLMs

He Wang | ICTP-AP, UCAS

Summary: AI for Gravitational Wave Science

Key Insights from Our Journey

Deep learning methods have transformed GW data analysis, enabling detection capabilities that complement traditional approaches
Evolution from simple CNN architectures to sophisticated frameworks that leverage domain knowledge
LLM-guided algorithmic optimization demonstrates potential for creating high-performance, interpretable methods
Balancing sensitivity and false alarm rates remains a key challenge
Benchmark results validate the potential of AI-driven approaches in scientific discovery

The Critical Role of Interpretability

Algorithm interpretability provides multiple essential benefits:

Scientific Understanding: Reveals unique characteristics of different model architectures and their decision processes
Algorithm Interpolation: Enables meaningful combination of different approaches by understanding their complementary strengths
Result Controllability: Provides confidence in outcomes and minimizes unexplained behaviors
Model Calibration: Allows fine-tuning of algorithms based on physical understanding rather than black-box optimization

The future of gravitational wave science lies at the intersection of traditional physics-inspired methods and interpretable AI approaches, creating a new paradigm for reliable scientific discovery.

for _ in range(num_of_audiences):
    print('Thank you for your attention! 🙏')

hewang@ucas.ac.cn

Interpretable Gravitational Wave Data Analysis with DL and LLMs

Interpretable Gravitational Wave Data Analysis with Deep Learning and Large Language Models

Gravitational Wave Astronomy

Challenge and Methodology: Detecting Signals in GW Data

Why should you consider applying ML to gravitational wave astrophysics?

The "Real" Reasons We Apply ML to GW Astrophysics

Why is AI/ML Everywhere in GW Research?

ML is FAST

ML is ACCURATE*

ML is FLEXIBLE

Is It Really So Simple?

Why Even Use AI?

Universal Approximation Theorem

How do we understand AI's inner workings in GW data analysis?

CNN for GW Detection: Pioneering Approaches

CNN for GW Detection: Feature Extraction

CNN for GW Detection: Feature Extraction

CNN for GW Detection: Feature Extraction

CNN for GW Detection: Feature Extraction

CNN for GW Detection: Feature Extraction

First Benchmark for GW Detection Algorithms

Interpretability Challenges: Comparing Detection Statistics

Interpretability Challenges: Discoveries vs. Validation (part 1/2)

Interpretability Challenges: Discoveries vs. Validation (part 1/2)

Interpretability Challenges: Discoveries vs. Validation (part 2/2)

Theoretical Understanding of Generative Models

Deep Learning is Not As Impressive As you Think, It's Mere Interpolation.

AI for Science

AI for Science

Why Do We Trust Traditional Models?

Bridging Trust & Performance in GW Analysis

Automated Heuristic Design: Problem Definition

Automated Heuristic Design: Problem Definition

Algorithmic Exploration：LLM Prompt Engineering

Algorithmic Synergy: MCTS, Evolution & LLM Agents

Algorithmic Synergy: MCTS, Evolution & LLM Agents

MLGWSC1: Algorithm Evolutionary Tree Visualization

MLGWSC1 Benchmark: Optimization Performance Results

MLGWSC1 Benchmark: Optimization Performance Results

Interpretability Analysis: PT Level 5

Interpretability Analysis: Generalization

Framework Mechanism Analysis

Bridging Trust & Performance in GW Analysis

Summary: AI for Gravitational Wave Science

Summary: AI for Gravitational Wave Science

Key Questions

The Rise of LLMs: How Code Training Transformed AI Capabilities

The Optimization Potential of Large Language Models

Theoretical Understanding of LLMs' Emergent Abilities

Key Questions

Iterative Inference: The New Frontier of LLM Scaling

Bridging Trust & Performance in GW Analysis

Summary: AI for Gravitational Wave Science

Summary: AI for Gravitational Wave Science

Interpretable Gravitational Wave Data Analysis with Deep Learning and Large Language Models

Interpretable Gravitational Wave Data Analysis with Deep Learning and Large Language Models

He Wang PRO

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