参考文献

[1]

A. G. Abac et al. GWTC-4.0: Updating the gravitational-wave transient catalog with observations from the first part of the fourth LIGO-Virgo-KAGRA observing run. arXiv preprint, 2025.

[2]

R. A. Hulse and J. H. Taylor. Discovery of a pulsar in a binary system. The Astrophysical Journal, 195:L51–L53, 1975.

[3]

B. P. Abbott et al. Observation of gravitational waves from a binary black hole merger. Physical Review Letters, 116:061102, 2016.

[4]

J. Aasi et al. Advanced LIGO. Classical and Quantum Gravity, 32:074001, 2015.

[5]

F. Acernese et al. Advanced Virgo: a second-generation interferometric gravitational wave detector. Classical and Quantum Gravity, 32:024001, 2015.

[6]

R. Abbott et al. GWTC-3: Compact binary coalescences observed by LIGO and Virgo during the second part of the third observing run. Physical Review X, 13:041039, 2023.

[7]

B. P. Abbott et al. GW170817: Observation of gravitational waves from a binary neutron star inspiral. Physical Review Letters, 119:161101, 2017.

[8]

Shang-Jie Jin, Ji-Yu Song, Tian-Yang Sun, Si-Ren Xiao, He Wang, Ling-Feng Wang, Jing-Fei Zhang, and Xin Zhang. Gravitational wave standard sirens: A brief review of cosmological parameter estimation. arXiv preprint, 2025.

[9]

Pau Amaro-Seoane et al. Laser interferometer space antenna. arXiv preprint, 2017.

[10]

Wen-Rui Hu and Yue-Liang Wu. The Taiji program in space for gravitational wave physics and the nature of gravity. National Science Review, 4(5):685–686, 2017.

[11]

Wen-Hong Ruan, Zong-Kuan Guo, Rong-Gen Cai, and Yuan-Zhong Zhang. Taiji program: Gravitational-wave sources. Results in Physics, 16:102918, 2020.

[12]

王赫, 杜明辉, 徐鹏, and 周宇峰. 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. 中国科学：物理学 力学 天文学, 54:270403, 2024.

[13]

Jun Luo et al. TianQin: a space-borne gravitational wave detector. Classical and Quantum Gravity, 33:035010, 2016.

[14]

Jianwei Mei et al. The TianQin project: current progress on science and technology. Progress of Theoretical and Experimental Physics, 2021(5):05A108, 2021.

[15]

Minghui Du, Pengcheng Wang, Ziren Luo, Wen-Biao Han, Xin Zhang, Xian Chen, Zhoujian Cao, Yonghe Zhang, He Wang, et al. Towards realistic detection pipelines of Taiji: New challenges in data analysis and high-fidelity simulations of space-based gravitational wave antenna. Science China: Physics, Mechanics &Astronomy, 69:249501, 2026.

[16]

M. Punturo et al. The Einstein Telescope: a third-generation gravitational wave observatory. Classical and Quantum Gravity, 27:194002, 2010.

[17]

Matthew Evans et al. A horizon study for cosmic explorer: Science, observatories, and community. arXiv preprint, 2021.

[18]

Marlin B. Schäfer, Ondřej Zelenka, Alexander H. Nitz, He Wang, Shichao Wu, Zong-Kuan Guo, Zhoujian Cao, Zhixiang Ren, et al. First machine learning gravitational-wave search mock data challenge. Physical Review D, 107:023021, 2023.

[19]

Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, and Bernhard Schölkopf. Real-time gravitational wave science with neural posterior estimation. Physical Review Letters, 127:241103, 2021.

[20]

He Wang, Shichao Wu, Zhoujian Cao, Xiaolin Liu, and Jian-Yang Zhu. Gravitational-wave signal recognition of LIGO data by deep learning. Physical Review D, 101:104003, 2020.

[21]

He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, and Zhixiang Ren. Sampling with prior knowledge for high-dimensional gravitational wave data analysis. Big Data Mining and Analytics, 5:53–63, 2022.

[22]

Minghui Du, Bo Liang, He Wang, Peng Xu, Ziren Luo, and Yueliang Wu. Advancing space-based gravitational wave astronomy: Rapid parameter estimation via normalizing flows. Science China: Physics, Mechanics &/span> Astronomy, 67:230412, 2024.

[23]

Bo Liang, Minghui Du, He Wang, Yuxiang Xu, Chang Liu, Xiaotong Wei, Peng Xu, Li-e Qiang, and Ziren Luo. Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows. Machine Learning: Science and Technology, 5:045040, 2024.

[24]

Bo Liang, Hong Guo, Tianyu Zhao, He Wang, Herik Evangelinelis, Yuxiang Xu, Chang Liu, Manjia Liang, Xiaotong Wei, Yong Yuan, Peng Xu, Minghui Du, Wei-Liang Qian, and Ziren Luo. Rapid parameter estimation for extreme mass ratio inspirals using machine learning. arXiv preprint, 2024.

[25]

Hui Sun, He Wang, and Jibo He. Accelerating Bayesian sampling for massive black hole binaries with prior constraints from conditional variational autoencoder. arXiv preprint, 2025.

[26]

Bo Liang and He Wang. Recent advances in simulation-based inference for gravitational wave data analysis. Astronomical Techniques and Instruments, 3:93–111, 2026.

[27]

He Wang, Yue Zhou, Zhoujian Cao, Zongkuan Guo, and Zhixiang Ren. WaveFormer: Transformer-based denoising method for gravitational-wave data. Machine Learning: Science and Technology, 5:015046, 2024.

[28]

Yuxiang Xu, He Wang, Minghui Du, Bo Liang, and Peng Xu. Gravitational wave signal denoising and merger time prediction by deep neural network. arXiv preprint, 2024.

[29]

Yuxiang Xu, Minghui Du, Peng Xu, Bo Liang, and He Wang. Gravitational wave signal extraction against non-stationary instrumental noises with deep neural network. Physics Letters B, 858:139016, 2024.

[30]

He Wang and Liang Zeng. Automated algorithmic discovery for scientific computing through LLM-guided evolutionary search: A case study in gravitational-wave detection. Nature Communications, 2025. Under review.

[31]

Baoyun Zhao, He Wang, and Liang Zeng. G-LNS: Generative large neighborhood search for LLM-based automatic heuristic design. arXiv preprint, 2025.

[32]

Yu-Xin Wang, Xiaotong Wei, Chun-Yue Li, Tian-Yang Sun, Shang-Jie Jin, He Wang, Jing-Lei Cui, Jing-Fei Zhang, and Xin Zhang. Search for exotic gravitational wave signals beyond general relativity using deep learning. Physical Review D, 112:024030, 2025.

[33]

Tian-Yang Sun, Yue Shao, Yichao Li, Yidong Xu, He Wang, and Xin Zhang. Deep learning-driven likelihood-free parameter inference for 21-cm forest observations. Communications Physics, 2025.

[34]

Albert Einstein. Näherungsweise Integration der Feldgleichungen der Gravitation. Sitzungsberichte der Königlich Preußischen Akademie der Wissenschaften, pages 688–696, 1916.

[35]

Luc Blanchet. Gravitational radiation from post-Newtonian sources and inspiralling compact binaries. Living Reviews in Relativity, 17:2, 2014.

[36]

Stanislav Babak et al. Validating the EOBNR waveform models for spinning, precessing binary black holes. Physical Review D, 95:024010, 2017.

[37]

Nelson Christensen and Renate Meyer. Parameter estimation with gravitational waves. Reviews of Modern Physics, 94:025001, 2022.

[38]

Laura K. Nuttall. Characterizing transient noise in the LIGO detectors. Philosophical Transactions of the Royal Society A, 376:20170286, 2018.

[39]

A. Buikema et al. Sensitivity and performance of the advanced LIGO detectors in the third observing run. Physical Review D, 102:062003, 2020.

[40]

P. B. Covas et al. Identification and mitigation of narrow spectral artifacts that degrade searches for persistent gravitational waves in the first two observing runs of advanced LIGO. Physical Review D, 97:082002, 2018.

[41]

Curt Cutler and Éanna É. Flanagan. Gravitational waves from merging compact binaries: How accurately can one extract the binary’s parameters from the inspiral waveform? Physical Review D, 49:2658–2697, 1994.

[42]

J. Veitch et al. Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library. Physical Review D, 91:042003, 2015.

[43]

Gregory Ashton et al. BILBY: A user-friendly Bayesian inference library for gravitational-wave astronomy. The Astrophysical Journal Supplement Series, 241:27, 2019.

[44]

John Skilling. Nested sampling for general Bayesian computation. Bayesian Analysis, 1:833–859, 2006.

[45]

Derek Davis and Marissa Walker. Detector characterization and mitigation of noise in ground-based gravitational-wave interferometers. Galaxies, 10(1):12, 2022.

[46]

Bruce Allen, Warren G. Anderson, Patrick R. Brady, Duncan A. Brown, and Jolien D. E. Creighton. FINDCHIRP: An algorithm for detection of gravitational waves from inspiraling compact binaries. Physical Review D, 85:122006, 2012.

[47]

Samantha A. Usman et al. The PyCBC search for gravitational waves from compact binary coalescence. Classical and Quantum Gravity, 33:215004, 2016.

[48]

Alexander H. Nitz et al. PyCBC inference: A python-based parameter estimation toolkit for compact binary coalescence signals. The Astrophysical Journal, 922:76, 2021.

[49]

Cody Messick et al. Analysis framework for the prompt discovery of compact binary mergers in gravitational-wave data. Physical Review D, 95:042001, 2017.

[50]

Kipp Cannon et al. Toward early-warning detection of gravitational waves from compact binary coalescence. The Astrophysical Journal, 748:136, 2012.

[51]

B. P. Abbott et al. Binary black hole mergers in the first advanced LIGO observing run. Physical Review X, 6:041015, 2016.

[52]

S. Klimenko et al. Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors. Physical Review D, 93:042004, 2016.

[53]

Neil J. Cornish and Tyson B. Littenberg. BayesWave: Bayesian inference for gravitational wave bursts and instrument glitches. Classical and Quantum Gravity, 32:135012, 2015.

[54]

John Skilling. Nested sampling. In AIP Conference Proceedings, volume 735, pages 395–405, 2004.

[55]

F. Feroz, M. P. Hobson, and M. Bridges. MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics. Monthly Notices of the Royal Astronomical Society, 398:1601–1614, 2009.

[56]

Joshua S. Speagle. dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences. Monthly Notices of the Royal Astronomical Society, 493:3132–3158, 2020.

[57]

Max Planck Institute for Gravitational Physics (Albert Einstein Institute). MLGWSC-1. https://www.aei.mpg.de/969193/mlgwsc-1, 2022. Accessed 2026-05-25.

[58]

Christopher J. Moore, Davide Gerosa, and Antoine Klein. Are stellar-mass black-hole binaries too quiet for LISA? Monthly Notices of the Royal Astronomical Society: Letters, 488(1):L94–L99, 2019.

[59]

Frans Pretorius. Evolution of binary black-hole spacetimes. Physical Review Letters, 95:121101, 2005.

[60]

Michael Boyle et al. The SXS collaboration catalog of binary black hole simulations. Classical and Quantum Gravity, 36:195006, 2019.

[61]

James Healy, Carlos O. Lousto, Jacob Lange, Richard O’Shaughnessy, Yosef Zlochower, and Manuela Campanelli. Second RIT binary black hole simulations catalog and its application to gravitational wave astronomy. Physical Review D, 105:124010, 2022.

[62]

Karan Jani, James Healy, James A. Clark, Lionel London, Pablo Laguna, and Deirdre Shoemaker. Georgia tech catalog of gravitational waveforms. Classical and Quantum Gravity, 33:204001, 2016.

[63]

Roberto Cotesta, Alessandra Buonanno, Alejandro Bohé, Andrea Taracchini, Ian Hinder, and Serguei Ossokine. Enriching the IMRPhenomD waveform model with subdominant multipoles: The inspiral. Physical Review D, 98:084028, 2018.

[64]

Jonathan Blackman, Scott E. Field, Mark A. Scheel, Chad R. Galley, Daniel A. Hemberger, Patricia Schmidt, and Rory Smith. A surrogate model of gravitational waveforms from numerical relativity simulations of precessing binary black hole mergers. Physical Review D, 95:104023, 2017.

[65]

Vijay Varma, Scott E. Field, Mark A. Scheel, Jonathan Blackman, Davide Gerosa, Leo C. Stein, Lawrence E. Kidder, and Harald P. Pfeiffer. Surrogate models for precessing binary black hole simulations with unequal masses. Physical Review Research, 1:033015, 2019.

[66]

Vijay Varma, Scott E. Field, Mark A. Scheel, Jonathan Blackman, Lawrence E. Kidder, and Harald P. Pfeiffer. Surrogate model of hybridized numerical relativity binary black hole waveforms. Physical Review D, 99:064045, 2019.

[67]

Alejandro Bohé et al. Improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors. Physical Review D, 95:044028, 2017.

[68]

Geraint Pratten et al. Computationally efficient models for the dominant and subdominant harmonic modes of precessing binary black holes. Physical Review D, 103:104056, 2021.

[69]

Michele Vallisneri. Use and abuse of the Fisher-information matrix in the assessment of gravitational-wave parameter-estimation prospects. Physical Review D, 77:042001, 2008.

[70]

Carl L. Rodriguez, Benjamin Farr, Will M. Farr, and Ilya Mandel. Inadequacies of the Fisher matrix in gravitational-wave parameter estimation. Physical Review D, 88:084013, 2013.

[71]

Stanislav Babak, Jonathan Gair, Alberto Sesana, Enrico Barausse, Carlos F. Sopuerta, Christopher P. L. Berry, Emanuele Berti, Pau Amaro-Seoane, Antoine Petiteau, and Antoine Klein. Science with the space-based interferometer LISA. V: Extreme mass-ratio inspirals. Physical Review D, 95:103012, 2017.

[72]

Curt Cutler and Michele Vallisneri. LISA detections of massive black hole inspirals: Parameter extraction errors due to inaccurate template waveforms. Physical Review D, 76:104018, 2007.

[73]

Elena Cuoco et al. Enhancing gravitational-wave science with machine learning. Machine Learning: Science and Technology, 2:011002, 2021.

[74]

Daniel George and E. A. Huerta. Deep learning for real-time attowave gravitational wave detection. Physics Letters B, 778:64–70, 2018.

[75]

Hunter Gabbard, Michael Williams, Fergus Hayes, and Chris Messenger. Matching matched filtering with deep networks for gravitational-wave astronomy. Physical Review Letters, 120:141103, 2018.

[76]

Timothy D. Gebhard, Niki Kilbertus, Ian Harry, and Bernhard Schölkopf. Convolutional neural networks: A magic bullet for gravitational-wave detection? Physical Review D, 100:063015, 2019.

[77]

Plamen G. Krastev. Real-time detection of gravitational waves from binary neutron stars using artificial neural networks. Physics Letters B, 803:135330, 2020.

[78]

Christoph Dreissigacker, Rahul Sharma, Chris Messenger, Ruining Zhao, and Reinhard Prix. Deep-learning continuous gravitational waves. Physical Review D, 100:044009, 2019.

[79]

iPhysResearch. GWDL: 匹配滤波-卷积神经网络(MF-CNN)模型的应用研究. https://iphysresearch.github.io/PhDthesis_html/C6/. Accessed 2026-05-26.

[80]

CunLiang Ma, Wei Wang, He Wang, and Zhoujian Cao. Ensemble of deep convolutional neural networks for real-time gravitational wave signal recognition. Physical Review D, 105:083013, 2022.

[81]

Cunliang Ma, Wei Wang, He Wang, and Zhoujian Cao. Artificial intelligence model for gravitational wave search based on the waveform envelope. Physical Review D, 107:063029, 2023.

[82]

Tianyu Zhao, Ruoxi Lyu, He Wang, Zhoujian Cao, and Zhixiang Ren. Space-based gravitational wave signal detection and extraction with deep neural network. Communications Physics, 6:212, 2023.

[83]

Wen-Hong Ruan, He Wang, Chang Liu, and Zong-Kuan Guo. Rapid search for massive black hole binary coalescences using deep learning. Physics Letters B, 841:137904, 2023.

[84]

Qianyun Yun, Wen-Biao Han, Yi-Yang Guo, He Wang, and Minghui Du. The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning. Science China: Physics, Mechanics &Astronomy, 68:210413, 2025.

[85]

Jun Tian, He Wang, Jibo He, Yu Pan, Shuo Cao, and Qingquan Jiang. Learning and interpreting gravitational-wave features from CNNs with a random forest approach. Machine Learning: Science and Technology, 6:035045, 2025.

[86]

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 618–626, 2017.

[87]

Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N. Balasubramanian. Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), pages 839–847, 2018.

[88]

Matthew D. Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. Lecture Notes in Computer Science, 8689:818–833, 2014.

[89]

James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13:281–305, 2012.

[90]

Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems (NeurIPS), volume 25, pages 2951–2959, 2012.

[91]

Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas. Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104(1):148–175, 2016.

[92]

Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. Neural architecture search: A survey. Journal of Machine Learning Research, 20(55):1–21, 2019.

[93]

Hanxiao Liu, Karen Simonyan, and Yiming Yang. DARTS: Differentiable architecture search. In International Conference on Learning Representations (ICLR), 2019.

[94]

Tianyu Zhao, Ruijun Shi, Yue Zhou, Zhoujian Cao, and Zhixiang Ren. Dawning of a new era in gravitational wave data analysis: Unveiling cosmic mysteries via artificial intelligence – a systematic review. Frontiers of Physics, 20(4):045301, 2025.

[95]

Kyle Cranmer, Johann Brehmer, and Gilles Louppe. The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, 117:30055–30062, 2020.

[96]

George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research, 22(57):1–64, 2021.

[97]

Danilo Jimenez Rezende and Shakir Mohamed. Variational inference with normalizing flows. 2015.

[98]

Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, and Jakob H. Macke. Neural importance sampling for rapid and reliable gravitational-wave inference. Physical Review Letters, 130:171403, 2023.

[99]

Stephen R. Green and Jonathan Gair. Complete parameter inference for GW150914 using deep learning. Machine Learning: Science and Technology, 2:03LT01, 2021.

[100]

Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Goncalves, and Jakob H. Macke. Benchmarking simulation-based inference. 2021.

[101]

Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real-valued non-volume preserving (Real NVP) transformations. In International Conference on Learning Representations (ICLR), 2017.

[102]

Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios. Neural spline flows. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, 2019.

[103]

Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. 2023.

[104]

Alejandro Torres-Forné, Pablo Cerdá-Durán, Martin Obergaulinger, Bernhard Müller, and José A. Font. Towards asteroseismology of core-collapse supernovae with gravitational-wave observations from the next generation of detectors. Physical Review Letters, 123:051102, 2019.

[105]

Michael W. Coughlin et al. Implications of the search for optical counterparts during the first six months of the advanced LIGO’s and advanced Virgo’s third observing run: Possible limits on the ejecta mass and binary properties. Monthly Notices of the Royal Astronomical Society, 492:863–876, 2020.

[106]

Wei Wei and E. A. Huerta. Gravitational wave denoising of binary black hole mergers with deep learning. Physics Letters B, 800:135081, 2020.

[107]

Chayan Chatterjee, Linqing Wen, Foivos Diakogiannis, and Kevin Vinsen. Extraction of binary black hole gravitational wave signals from detector data using deep learning. Physical Review D, 104:064046, 2021.

[108]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. 2015.

[109]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. 2017.

[110]

Aaron van den Oord et al. WaveNet: A generative model for raw audio. arXiv preprint, 2016.

[111]

Stéphane G. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674–693, 1989.

[112]

Ingrid Daubechies. Orthonormal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, 41(7):909–996, 1988.

[113]

David L. Donoho and Iain M. Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3):425–455, 1994.

[114]

Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M. Pawan Kumar, Emilien Dupont, Francisco J. R. Ruiz, Jordan S. Ellenberg, Pushmeet Kohli, Koray Kavukcuoglu, Demis Hassabis, and Alhussein Fawzi. Mathematical discoveries from program search with large language models. Nature, 625:468–475, 2024.

[115]

Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang. Evolution of heuristics: Towards efficient automatic algorithm design using large language model. In Proceedings of the 41st International Conference on Machine Learning (ICML), 2024.

[116]

Haoran Ye, Jiarui Wang, Zhiguang Cao, Federico Berto, Chuanbo Hua, Haeyeon Kim, Jinkyoo Park, and Guojie Song. Large language models as hyper-heuristics with reflective evolution. In Advances in Neural Information Processing Systems (NeurIPS), 2024.

[117]

Maziar Raissi, Paris Perdikaris, and George Em Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707, 2019.

[118]

Rishi Bommasani, Drew A. Hudson, Ehsan Aditi, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.

[119]

John Jumper, Richard Evans, Alexander Pritzel, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 596:583–589, 2021.

[120]

Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, et al. Learning skillful medium-range global weather forecasting. Science, 382(6677):1416–1421, 2023.

[121]

Samantha R. Cook, Andrew Gelman, and Donald B. Rubin. Validation of software for Bayesian models using posterior quantiles. Journal of Computational and Graphical Statistics, 15(3):675–692, 2006.

[122]

Alvin J. K. Chua, Michael L. Katz, Niels Warburton, and Leor Barack. Rapid generation of fully relativistic extreme-mass-ratio-inspiral waveforms for LISA data analysis. Physical Review Letters, 126:051102, 2021.

[123]

Michael L. Katz, Hugo Martel, and Shane L. Larson. Fast, accurate waveforms for compact binaries: Post-Newtonian waveforms for massive black hole binaries in LISA. Physical Review D, 102:023033, 2020.

[124]

Tian-Yang Sun, Tian-Nuo Li, He Wang, Jing-Fei Zhang, and Xin Zhang. Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra. arXiv preprint, 2025.

[125]

M. Tse, H. Yu, N. Kijbunchoo, et al. Quantum-enhanced advanced LIGO detectors in the era of gravitational-wave astronomy. Physical Review Letters, 123:231107, 2019.

[126]

L. Badurina, E. Bentine, D. Blas, et al. AION: An atom interferometer observatory and network. Journal of Cosmology and Astroparticle Physics, 2020:011, 2020.

[127]

Peter W. Graham, Jason M. Hogan, Mark A. Kasevich, Surjit Rajendran, and Roger W. Romani. Mid-band gravitational wave detection with precision atomic sensors. arXiv preprint, arXiv:1711.02225, 2017. arXiv:1711.02225.

[128]

Ming-Sheng Zhan, Jin Wang, Wei-Tou Ni, et al. ZAIGA: Zhaoshan long-baseline atom interferometer gravitation antenna. International Journal of Modern Physics D, 29:1940005, 2020.

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