2023年8月20日, 12:00-12:15

2023 中国物理学会秋季学术会议 | 中国·银川

针对引力波观测数据的智能噪声抑制

王赫

hewang@ucas.ac.cn

中国科学院大学 · 国际理论物理中心（亚太地区）

中国科学院大学 · 引力波宇宙太极实验室（北京/杭州）

Intelligent Noise Suppression for GW Observational Data

  • In 1916, A. Einstein proposed the GR and predicted the existence of GW.

  • Gravitational waves (GW) are a strong field effect in the GR.

    • 2015: the first experimental detection of GW from the merger of two black holes was achieved.

    • 2017: the first multi-messenger detection of a BNS signal was achieved, marking the beginning of multi-messenger astronomy.

    • 2017: the Nobel Prize in Physics was awarded for the detection of GW.

    • As of now: more than 90 gravitational wave events have been discovered.

    • O4, which began on May 24th 2023, is currently in progress.

双星并合系统产生的引力波波源

引力波振幅的测量

地面引力波探测器网络

2017 年诺贝尔物理学奖

Gravitational Wave Astronomy

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 

©Floor Broekgaarden (repo)

GWTC-3

  • AI for Science \(\rightarrow\) AI for GW
  • Artificial Intelligence (AI) has great potential to revolutionize gravitational wave astronomy by improving data analysis, modeling, and detector development.

AI for Gravitational Wave

GW Data Challenge

  • GW Data characteristics:

    • ​Noise: non-Gaussian and non-stationary

    • Signal: A low signal-to-noise ratio (SNR) which is typically about 1/100 of the noise amplitude (-60 dB)

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

CQG. 37 (2020) 055002

  • GW Data characteristics:

    • ​Noise: non-Gaussian and non-stationary

    • Signal: A low signal-to-noise ratio (SNR) which is typically about 1/100 of the noise amplitude (-60 dB)

Data quality improvement

Credit: Marco Cavaglià 

LIGO-Virgo data processing

GW searches

Astrophsical interpretation of GW sources

CQG. 37 (2020) 055002

GW Data Challenge

Noise Suppression: Data Quality Improvement

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

BEFORE

AFTER

Noise Suppression: Data Quality Improvement

BEFORE

AFTER

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

Noise Suppression: Data Quality Improvement

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

Bacon P. et al.  arXiv: 2205.13513

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

Noise Suppression: Data Quality Improvement

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

Bacon P. et al.  arXiv: 2205.13513

Murali C & Lumley D. arXiv: 2210.01718

Wei W and Huerta E A. PLB 2020

Chatterjee C, Wen L, et al. PRD 2021

Noise Suppression: Data Quality Improvement

  • Billion-scale transformer-based model (WaveFormer)
    • Suppression on realistic noise, and
    • Recovery of injections / GW events

arXiv:2212.14283

DOI: 10.21203/rs.3.rs-2452860/v1

Noise Suppression: Adaptive Approach for Multimodal Source

  • Noise suppression on multimodal sources for space-based scenarios.
    • "One Model to Rule Them All".

 T.Z, R.L, H.W, et al. "Space-based gravitational wave signal detection and extraction with deep neural network" Communications Physics, (2023)

Credit: ESA, K. Holley-Bockelmann

Key Takeaways

  1. Gravitational-wave astronomy turns to AI:
    • A thriving and highly competitive research field on the international stage.
  2. AI as a Revolutionary Pathway for Scientific Discoveries:
    • Building noise models using AI as a crucial breakthrough for the future.
  3. Future Research Directions:
    • Achieving rapid and adaptive noise modeling on complex empirical GW data.
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This slide: https://slides.com/iphysresearch/2023aug_denoising

Smith, Rory. Nature Physics 18, 1 (2022): 9–11