Various types of Glitch

• The improvement of data quality is a very complex issue, with data from over 20,000 sensor channels determining the quality of the gravitational wave science data channel.

• Reducing non-Gaussian short-duration pulse interference (Glitches) in gravitational wave data will help reduce the false alarm rate of gravitational wave signals.

• Removing Glitches from gravitational wave detection data is a multi-classification problem.

  • 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.

Credit: Marco Cavaglià 

CQG. 37 (2020) 055002

• Extraction and denoising GW signals using deep learning:
  • Both Wei et al. [PLB 2020] and Chatterjee et al. [PRD 2021] have shown that considering phase overlaps yields excellent results.
• Detecting and denoising GW signals using deep learning:
  • Both Bacon et al. [MLST 2023] and Murali et al. [PRD 2023] could recover the phase of original GW signal with certain cycles but failed to recover the complete evaluation in amplitude scale.

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

• Implementations:
  • input size: 8.0625 sec
  • fs = ​2048Hz
  • Band-pass: 20~2048Hz
• Highlights
  • PSD sampling from real noise.
  • Amplitude-preserving normalization
  • Masked loss
  • Never-repeating sampling for
    training

Strain

Whiten

Normalized

 ∼\(10^{−19}\)

 ∼\(10^{2}\)

 ∼\(10^{0}\)

32 s

32 s

merger

\(t_c\) (e.g. near GW150914)

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

MSE-Loss\(_i\)

\(std\)

[1, 128, 256]

Noise\(_i\):

Signal\(_i\):

Input\(_i\):

Label\(_i\):

Output\(_i\):

8.0625 s

8.0625 s

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

• Implementations:
  • input size: 8.0625 sec
  • fs = ​2048Hz
  • Band-pass: 20~2048Hz
• Highlights
  • PSD sampling from real noise
  • Amplitude-preserving normalization
  • Masked loss
  • Never-repeating sampling for
    training

Strain

Whiten

Normalized

 ∼\(10^{−19}\)

 ∼\(10^{2}\)

 ∼\(10^{0}\)

32 s

32 s

merger

\(t_c\) (e.g. near GW150914)

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

MSE-Loss\(_i\)

\(std\)

[1, 128, 256]

Noise\(_i\):

Signal\(_i\):

Input\(_i\):

Label\(_i\):

Output\(_i\):

8.0625 s

8.0625 s

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

Strain

Whiten

Normalized

 ∼\(10^{−19}\)

 ∼\(10^{2}\)

 ∼\(10^{0}\)

32 s

32 s

merger

\(t_c\) (e.g. near GW150914)

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

MSE-Loss\(_i\)

\(std_i\)

[1, 128, 256]

Noise\(_i\):

Signal\(_i\):

Input\(_i\):

Label\(_i\):

Output\(_i\):

8.0625 s

8.0625 s

• Implementations:
  • input size: 8.0625 sec
  • fs = ​2048Hz
  • Band-pass: 20~2048Hz
• Highlights
  • PSD sampling from real noise.
  • Amplitude-preserving normalization
  • Masked loss
  • Never-repeating sampling for
    training

• Implementations:
  • input size: 8.0625 sec
  • fs = ​2048Hz
  • Band-pass: 20~2048Hz
• Highlights
  • PSD sampling from real noise.
  • Amplitude-preserving normalization
  • Masked loss
  • Never-repeating sampling for training

• Timestamp distribution of instances in the "memory pool"

• Epoch-wise loss & BBH test overlap

• Suppression on realistic noise including glitches
• Recovery of injections / GW events

Effect on Realistic Noise (Blip)

Effect on pure noise

Effect on glitches

• Suppression on realistic noise including glitches
• Recovery of injections / GW events
• Data quality improvement and significance estimates

• 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.

• 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.

Waveformer (OURs)

LVK. PRD (2016). arXiv:1602.03839

• 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.

• 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.
     
• Future Directions
  • Construct a comprehensive GW signal search pipeline for BBH/BNS/NSBH events.
  • Explore the use of ensemble learning and other statistical methods to enhance the interpretability of the AI detection pipeline and address issues related to its validity.

A Python Toolbox for Gravitational Wave Astronomy: GWToolkit​

• This toolbox, powered by Ray/JAX, supports both CPU and GPU. It is specifically designed for machine learning applications in gravitational wave astronomy, providing efficient and scalable tools for data analysis and model training.
   

Can AI identify new GW events from LIGO data?

• Exploring the potential of AI to detect new gravitational wave events from LIGO data.
• Could these signals indicate phenomena beyond General Relativity (bGR) or do they exhibit eccentricity?

Mitigating bias in AI-Driven GW data analysis

• 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)

Menéndez-Vázquez A, et al. PRD 2021

• 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.

• 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​)

• 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

• 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).

• 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.

• 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)

• 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 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.

• 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 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.

• Through time shift, background analysis is done on other triggers around the target trigger. (time-shift interval 0.1 sec)
• Finally, by counting the number of false alarm trigger pairs, we obtain the IFAR value of the target trigger, which represents the reported or candidate BBH event in this experiment.

Waveformer (OURs)

(PyCBC) Davies​, et al. PRD 2020