Taiji data challenge for exploring gravitational wave universe

doi:10.1007/s11467-023-1318-y

Front. Phys.

2023, Vol. 18

Issue (6) : 64302 https://doi.org/10.1007/s11467-023-1318-y

RESEARCH ARTICLE

Taiji data challenge for exploring gravitational wave universe

Zhixiang Ren^1,², Tianyu Zhao^1,^3,², Zhoujian Cao^1,^3,⁴(

), Zong-Kuan Guo^1,^5,^6,⁴, Wen-Biao Han^1,^4,^7,^8,^9,¹⁰, Hong-Bo Jin^1,^11,⁸, Yue-Liang Wu^1,^5,^4,⁹(

)

¹. Taiji Laboratory for Gravitational Wave Universe, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
². Peng Cheng Laboratory, Shenzhen 518055, China
³. Department of Astronomy, Beijing Normal University, Beijing 100875, China
⁴. School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
⁵. CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
⁶. School of Physical Sciences, UCAS, Beijing 100049, China
⁷. Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
⁸. School of Astronomy and Space Science, UCAS, Beijing 100049, China
⁹. International Centre for Theoretical Physics Asia-Pacific (ICTP-AP, UNESCO), UCAS, Beijing 100190, China
¹⁰. Shanghai Frontiers Science Center for Gravitational Wave Detection, Shanghai 200240, China
¹¹. Key Laboratory of Computational Astrophysics, National Astronomical Observatories, Beijing 100101, China

Download: PDF(8655 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The direct observation of gravitational waves (GWs) opens a new window for exploring new physics from quanta to cosmos and provides a new tool for probing the evolution of universe. GWs detection in space covers a broad spectrum ranging over more than four orders of magnitude and enables us to study rich physical and astronomical phenomena. Taiji is a proposed space-based gravitational wave (GW) detection mission that will be launched in the 2030s. Taiji will be exposed to numerous overlapping and persistent GW signals buried in the foreground and background, posing various data analysis challenges. In order to empower potential scientific discoveries, the Mock Laser Interferometer Space Antenna (LISA) data challenge and the LISA data challenge (LDC) were developed. While LDC provides a baseline framework, the first LDC needs to be updated with more realistic simulations and adjusted detector responses for Taiji’s constellation. In this paper, we review the scientific objectives and the roadmap for Taiji, as well as the technical difficulties in data analysis and the data generation strategy, and present the associated data challenges. In contrast to LDC, we utilize second-order Keplerian orbit and second-generation time delay interferometry techniques. Additionally, we employ a new model for the extreme-mass-ratio inspiral waveform and stochastic GW background spectrum, which enables us to test general relativity and measure the non-Gaussianity of curvature perturbations. Furthermore, we present a comprehensive showcase of parameter estimation using a toy dataset. This showcase not only demonstrates the scientific potential of the Taiji data challenge (TDC) but also serves to validate the effectiveness of the pipeline. As the first data challenge for Taiji, we aim to build an open ground for data analysis related to Taiji sources and sciences. More details can be found on the official website (taiji-tdc.ictp-ap.org).

Keywords gravitational wave universe evolution Taiji data challenge

Corresponding Author(s): Zhoujian Cao,Yue-Liang Wu

Issue Date: 10 August 2023

Cite this article:

Zhixiang Ren,Tianyu Zhao,Zhoujian Cao, et al. Taiji data challenge for exploring gravitational wave universe[J]. Front. Phys. , 2023, 18(6): 64302.

URL:

https://academic.hep.com.cn/fop/EN/10.1007/s11467-023-1318-y
https://academic.hep.com.cn/fop/EN/Y2023/V18/I6/64302

Tab.1 Scientific objectives of Taiji mission.

Tab.2 Technical challenges of Taiji mission.

Fig.1 Time-domain data of all the TDC datasets. The strain of TDI X channel data and signals are presented. (a) TDC-1, the MBHB signal is zoomed in because of its duration. (b, c) TDC-2-1 and TDC-2-2. (d) TDC-3. (e) TDC-4. (f) TDC-5. (g, h) TDC-6-1 and TDC-6-2. (i) TDC-7, signals No. 8 and No. 9 are zoomed in due to the closeness of their coalescence time.

Fig.2 Frequency domain data of all the TDC datasets. The PSD of TDI X channel data and signals are compared with the noise PSD. (a) TDC-1. (b, c) TDC-2-1 and TDC-2-2. (d) TDC-3. (e) TDC-4. (f) TDC-5. (g, h) TDC-6-1 and TDC-6-2. (i) TDC-7.

No.	$M T (M ⊙)$	$q$	$s 1$	$s 2$	$D L$ (Gpc)	$ι$	$ψ$	$λ$	$β$	$t c (y r)$	$? c$

1	$1.2 × 106$	0.3	0.1	0.2	53.39	$2 π / 3$	$π / 3$	$π / 10$	$π / 8$	$0.2$	1
2	$1.8 × 106$	0.8	0.14	0.12	16.02	$π$	$π / 3$	$π / 15$	$5 π / 4$	$0.4$	0.05
3	$2.4 × 106$	0.4	0.18	0.08	21.36	$5 π / 6$	$π / 3$	$π / 20$	$7 π / 4$	$0.6$	0.1
4	$1.5 × 106$	0.7	0.14	0.8	53.39	$π / 3$	$π / 3$	$π / 25$	$3 π / 4$	$0.8$	0.15
5	$6 × 105$	0.5	0.2	0.4	53.39	$π / 3$	$π / 3$	$π / 5$	$π / 4$	$1$	0.5
6	$1.2 × 106$	0.6	0.1	0.6	106.78	$π / 6$	$2 π / 3$	$π / 30$	$5 π / 4$	$1.2$	0.35
7	$2.4 × 106$	0.1	0.22	0.48	16.02	$4 π / 15$	$5 π / 3$	$π / 35$	$π / 8$	$1.4$	0.45
8	$1.2 × 106$	0.05	0.16	0.28	26.69	$13 π / 30$	$π / 3$	$2 π / 5$	$π / 12$	$1.6$	1
9	$1.8 × 106$	0.5	0.02	0.08	32.03	$14 π / 30$	$π / 3$	$3 π / 5$	$11 π / 40$	$1.601$	0.4
10	$1.2 × 107$	1	0.06	0.08	10.68	$π / 3$	$π / 3$	$6 π / 5$	$π / 4$	$1.8$	0.5

Tab.3 Parameters of MBHB signals used in TDC-1 and TDC-7.

Name	Scientific objective	Technical challenge	Signals	Waveform model

TDC-1	1, 5, 6	7	1 MBHB signal	SEOBNRv4_opt
TDC-2-1	2, 5, 6	7	1 EMRI signal	PN5AAK
TDC-2-2	2, 5, 6	7	1 EMRI signal	XSPEG
TDC-3	3, 5, 6	7	43 VGB signals	Sinusoidal
TDC-4	3, 5, 6	1, 7	$3 × 107$ GB signals	Sinusoidal
TDC-5	4, 5, 6	2, 7	$2 × 105$ SOBBH signals	IMRPhenomD
TDC-6-1	5, 6, 7	7	SGWB	Power-law
TDC-6-2	5, 6, 7	7	SGWB	Non-Gaussian curvature perturbations
TDC-7	1, 3, 5, 6	1, 3, 7	10 MBHBs, 43 VGBs, $3 × 107 GBs$	SEOBNRv4_opt, Sinusoidal

Tab.4 Summary of Taiji data challenge datasets.

Fig.3 MCMC posterior corner plot for the toy dataset. The MCMC posterior analysis is conducted on our toy dataset, utilizing the following source parameter values:

(A, f, f ˙, λ, β, ι, ? 0, ψ) = (2.008192 × 10 ? 23, 0.00459585, 1.469823 × 10 ? 16, 4.604082,

? 0.052144, 0.39811, 1.8777623071795864, 2.927109)

. The posterior distribution is represented by the dark cyan color, while the true parameter values are indicated by the red lines.

1	P. Abbott B. , (LIGO Scientific Collaboration . , Collaboration) Virgo . et al.. Observation of gravitational waves from a binary black hole merger. Phys. Rev. Lett., 2016, 116(6): 061102 https://doi.org/10.1103/PhysRevLett.116.061102
2	LIGO Scientific Collaboration TheVirgo Collaboration theKAGRA Collaboration the, et al.., GWTC-3: Compact binary coalescences observed by LIGO and Virgo during the second part of the third observing run, arXiv: 2111.03606 (2021)
3	González G. , Viceré A. , Wen L. . Gravitational wave astronomy. Front. Phys., 2013, 8(6): 771 https://doi.org/10.1007/s11467-013-0329-5
4	Matichard F. , Lantz B. , Mittleman R. , Mason K. , Kissel J. . et al.. Seismic isolation of advanced LIGO: Review of strategy, instrumentation and performance. Class. Quantum Gravity, 2015, 32(18): 185003 https://doi.org/10.1088/0264-9381/32/18/185003
5	Amaro-Seoane P., et al.., Laser interferometer space antenna, arXiv: 1702.00786 (2017)
6	Gong X. , Xu S. , Bai S. , Cao Z. , Chen G. , Chen Y. , He X. , Heinzel G. , K. Lau Y. , Liu C. , Luo J. , Luo Z. , P. Patón A. , Rüdiger A. , Shao M. , Spurzem R. , Wang Y. , Xu P. , C. Yeh H. , Yuan Y. , Zhou Z. . A scientific case study of an advanced LISA mission. Class. Quantum Gravity, 2011, 28(9): 094012 https://doi.org/10.1088/0264-9381/28/9/094012
7	Luo Z. , Wang Y. , Wu Y. , Hu W. , Jin G. . The Taiji program: A concise overview. Prog. Theor. Exp. Phys., 2021, 2021(5): 05A108 https://doi.org/10.1093/ptep/ptaa083
8	L. Wu Y., in: Presentation to 1st eLISA Consortium Meeting (2012)
9	Q. Li Y. , R. Luo Z. , S. Liu H. , H. Dong Y. , Jin G. . Laser interferometer used for satellite—satellite tracking: An on-ground methodological demonstration. Chin. Phys. Lett., 2012, 29(7): 079501 https://doi.org/10.1088/0256-307X/29/7/079501
10	S. Liu H. , H. Dong Y. , Q. Li Y. , R. Luo Z. , Jin G. . The evaluation of phasemeter prototype performance for the space gravitational waves detection. Rev. Sci. Instrum., 2014, 85(2): 024503 https://doi.org/10.1063/1.4865121
11	H. Dong Y. , S. Liu H. , R. Luo Z. , Q. Li Y. , Jin G. . Methodological demonstration of laser beam pointing control for space gravitational wave detection missions. Rev. Sci. Instrum., 2014, 85(7): 074501 https://doi.org/10.1063/1.4891037
12	Q. Li Y. , R. Luo Z. , S. Liu H. , H. Dong Y. , Jin G. . Path-length measurement performance evaluation of polarizing laser interferometer prototype. Appl. Phys. B, 2015, 118(2): 309 https://doi.org/10.1007/s00340-014-5987-7
13	R. Hu W. , L. Wu Y. . The Taiji Program in Space for gravitational wave physics and the nature of gravity. Natl. Sci. Rev., 2017, 4(5): 685 https://doi.org/10.1093/nsr/nwx116
14	Luo Z. , Guo Z. , Jin G. , Wu Y. , Hu W. . A brief analysis to Taiji: Science and technology. Results Phys., 2020, 16: 102918 https://doi.org/10.1016/j.rinp.2019.102918
15	Luo Z. , Wang Q. , Mahrdt C. , Goerth A. , Heinzel G. . Possible alternative acquisition scheme for the gravity recovery and climate experiment follow-on-type mission. Appl. Opt., 2017, 56(5): 1495 https://doi.org/10.1364/AO.56.001495
16	Luo Z. , Liu H. , Jin G. . The recent development of interferometer prototype for Chinese gravitational wave detection pathfinder mission. Opt. Laser Technol., 2018, 105: 146 https://doi.org/10.1016/j.optlastec.2018.02.042
17	Liu H. , Dong Y. , Gao R. , Luo Z. , Jin G. . Principle demonstration of the phase locking based on the electro-optic modulator for Taiji space gravitational wave detection pathfinder mission. Opt. Eng., 2018, 57(5): 054113 https://doi.org/10.1117/1.OE.57.5.054113
18	Liu H. , Luo Z. , Jin G. . The development of phasemeter for Taiji space gravitational wave detection. Microgravity Sci. Technol., 2018, 30(6): 775 https://doi.org/10.1007/s12217-018-9625-6
19	Deng W. , Yang T. , Cao J. , Zang E. , Li L. , Chen L. , Fang Z. . High-efficiency 1064 nm nonplanar ring oscillator Nd:YAG laser with diode pumping at 885 nm. Opt. Lett., 2018, 43(7): 1562 https://doi.org/10.1364/OL.43.001562
20	Wang Z. , Sha W. , Chen Z. , S. Kang Y. , R. Luo Z. , Li M. , P. Li Y. . Preliminary design and analysis of telescope for space gravitational wave detection. Chin. Opt., 2018, 11(1): 131 https://doi.org/10.3788/co.20181101.0131
21	Taiji Scientific Collaboration The . China’s first step towards probing the expanding universe and the nature of gravity using a space borne gravitational wave antenna. Commun. Phys., 2021, 4: 34 https://doi.org/10.1038/s42005-021-00529-z
22	Taiji Scientific Collaboration The . Taiji program in space for gravitational universe with the first run key technologies test in Taiji-1. Int. J. Mod. Phys. A, 2021, 36: 2102002 https://doi.org/10.1142/S0217751X21020024
23	Klein A. , Barausse E. , Sesana A. , Petiteau A. , Berti E. , Babak S. , Gair J. , Aoudia S. , Hinder I. , Ohme F. , Wardell B. . Science with the space-based interferometer eLISA: Supermassive black hole binaries. Phys. Rev. D, 2016, 93(2): 024003 https://doi.org/10.1103/PhysRevD.93.024003
24	H. Zhang X. , D. Mohanty S. , B. Zou X. , X. Liu Y. . Resolving Galactic binaries in LISA data using particle swarm optimization and cross-validation. Phys. Rev. D, 2021, 104(2): 024023 https://doi.org/10.1103/PhysRevD.104.024023
25	Sesana A. . Prospects for multiband gravitational-wave astronomy after GW150914. Phys. Rev. Lett., 2016, 116(23): 231102 https://doi.org/10.1103/PhysRevLett.116.231102
26	Xin S. , B. Han W. , C. Yang S. . Gravitational waves from extreme-mass-ratio inspirals using general parametrized metrics. Phys. Rev. D, 2019, 100(8): 084055 https://doi.org/10.1103/PhysRevD.100.084055
27	Otto M., Time-Delay Interferometry Simulations for the Laser Interferometer Space Antenna, Ph. D. thesis, Hannover: Gottfried Wilhelm Leibniz Universität Hannover, 2015
28	Blelly A. , Bobin J. , Moutarde H. . Sparse data inpainting for the recovery of Galactic-binary gravitational wave signals from gapped data. Mon. Not. R. Astron. Soc., 2021, 509(4): 5902 https://doi.org/10.1093/mnras/stab3314
29	Robson T. , J. Cornish N. . Detecting gravitational wave bursts with LISA in the presence of instrumental glitches. Phys. Rev. D, 2019, 99(2): 024019 https://doi.org/10.1103/PhysRevD.99.024019
30	V. Dhurandhar S. , R. Nayak K. , Koshti S. , Y. Vinet J. . Fundamentals of the LISA stable flight formation. Class. Quantum Gravity, 2005, 22(3): 481 https://doi.org/10.1088/0264-9381/22/3/002
31	R. Nayak K. , Koshti S. , V. Dhurandhar S. , Y. Vinet J. . On the minimum flexing of LISA’s arms. Class. Quantum Gravity, 2006, 23(5): 1763 https://doi.org/10.1088/0264-9381/23/5/017
32	Wu B. , G. Huang C. , F. Qiao C. . Analytical analysis on the orbits of Taiji spacecrafts. Phys. Rev. D, 2019, 100(12): 122001 https://doi.org/10.1103/PhysRevD.100.122001
33	Chauvineau B. , Regimbau T. , Y. Vinet J. , Pireaux S. . Relativistic analysis of the LISA long range optical links. Phys. Rev. D, 2005, 72(12): 122003 https://doi.org/10.1103/PhysRevD.72.122003
34	Hees A. , Bertone S. , Le Poncin-Lafitte C. , formulation of coordinate light time Relativistic . Doppler, and astrometric observables up to the second post-Minkowskian order. Phys. Rev. D, 2014, 89(6): 064045 https://doi.org/10.1103/PhysRevD.89.064045
35	L. Katz M. , B. Bayle J. , J. K. Chua A. , Vallisneri M. . Assessing the data-analysis impact of LISA orbit approximations using a GPU-accelerated response model. Phys. Rev. D, 2022, 106(10): 103001 https://doi.org/10.1103/PhysRevD.106.103001
36	Rijnveld N.A. C. M. Pijnenburg J., in: International Conference on Space Optics — ICSO 2010, edited by N. Kadowaki, SPIE, Rhodes Island, Greece, 2017, p. 96
37	Babak S.Hewitson M.Petiteau A., LISA sensitivity and SNR calculations, arXiv: 2108.01167 (2021)
38	L. Katz M. , Z. Kelley L. , Dosopoulou F. , Berry S. , Blecha L. , L. Larson S. . Probing massive black hole binary populations with LISA. Mon. Not. R. Astron. Soc., 2019, 491: 2301 https://doi.org/10.1093/mnras/stz3102
39	Bohé A. , Shao L. , Taracchini A. , Buonanno A. , Babak S. . et al.. Improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors. Phys. Rev. D, 2017, 95(4): 044028 https://doi.org/10.1103/PhysRevD.95.044028
40	R. Gair J. , Babak S. , Sesana A. , Amaro-Seoane P. , Barausse E. , P. L. Berry C. , Berti E. , Sopuerta C. . Prospects for observing extreme-mass-ratio inspirals with LISA. J. Phys. Conf. Ser., 2017, 840: 012021 https://doi.org/10.1088/1742-6596/840/1/012021
41	Babak S. , Gair J. , Sesana A. , Barausse E. , F. Sopuerta C. , P. L. Berry C. , Berti E. , Amaro-Seoane P. , Petiteau A. , Klein A. . Science with the space-based interferometer LISA. V. Extreme mass-ratio inspirals. Phys. Rev. D, 2017, 95(10): 103012 https://doi.org/10.1103/PhysRevD.95.103012
42	Barack L. , Cutler C. . Using LISA extreme-mass-ratio inspiral sources to test off-Kerr deviations in the geometry of massive black holes. Phys. Rev. D, 2007, 75(4): 042003 https://doi.org/10.1103/PhysRevD.75.042003
43	Glampedakis K. . Extreme mass ratio inspirals: LISA’s unique probe of black hole gravity. Class. Quantum Gravity, 2005, 22(15): S605 https://doi.org/10.1088/0264-9381/22/15/004
44	B. Han W. , Cao Z. . Constructing effective one-body dynamics with numerical energy flux for intermediate-mass-ratio inspirals. Phys. Rev. D, 2011, 84(4): 044014 https://doi.org/10.1103/PhysRevD.84.044014
45	Babak S. , Fang H. , R. Gair J. , Glampedakis K. , A. Hughes S. . “Kludge” gravitational waveforms for a test-body orbiting a Kerr black hole. Phys. Rev. D, 2007, 75(2): 024005 https://doi.org/10.1103/PhysRevD.75.024005
46	Barack L. , Cutler C. . LISA capture sources: Approximate waveforms, signal-to-noise ratios, and parameter estimation accuracy. Phys. Rev. D, 2004, 69(8): 082005 https://doi.org/10.1103/PhysRevD.69.082005
47	J. K. Chua A. , R. Gair J. . Improved analytic extreme-mass-ratio inspiral model for scoping out eLISA data analysis. Class. Quantum Gravity, 2015, 32(23): 232002 https://doi.org/10.1088/0264-9381/32/23/232002
48	J. K. Chua A. , J. Moore C. , R. Gair J. . Augmented kludge waveforms for detecting extreme-mass-ratio inspirals. Phys. Rev. D, 2017, 96(4): 044005 https://doi.org/10.1103/PhysRevD.96.044005
49	L. Katz M. , J. K. Chua A. , Speri L. , Warburton N. , A. Hughes S. . Fast extreme-mass-ratio-inspiral waveforms: New tools for millihertz gravitational-wave data analysis. Phys. Rev. D, 2021, 104(6): 064047 https://doi.org/10.1103/PhysRevD.104.064047
50	Zhang C. , B. Han W. , C. Yang S. . Analytical effective one-body formalism for extreme-mass-ratio inspirals with eccentric orbits. Commum. Theor. Phys., 2021, 73(8): 085401 https://doi.org/10.1088/1572-9494/abfbe4
51	Kupfer T. , Korol V. , Shah S. , Nelemans G. , R. Marsh T. , Ramsay G. , J. Groot P. , T. H. Steeghs D. , M. Rossi E. . LISA verification binaries with updated distances from Gaia Data Release 2. Mon. Not. R. Astron. Soc., 2018, 480(1): 302 https://doi.org/10.1093/mnras/sty1545
52	Nelemans G. , R. Yungelson L. , F. P. Zwart S. . The gravitational wave signal from the Galactic disk population of binaries containing two compact objects. Astron. Astrophys., 2001, 375(3): 890 https://doi.org/10.1051/0004-6361:20010683
53	Nelemans G. , A. Tout C. . Reconstructing the evolution of white dwarf binaries: Further evidence for an alternative algorithm for the outcome of the common-envelope phase in close binaries. Mon. Not. R. Astron. Soc., 2005, 356(2): 753 https://doi.org/10.1111/j.1365-2966.2004.08496.x
54	Gair J.Hewitson M.Petiteau A.Mueller G., in: Handbook of Gravitational Wave Astronomy, edited by C. Bambi, S. Katsanevas, and K. D. Kokkotas, Springer Singapore, Singapore, 2021, pp 1–71
55	Toubiana A. , Marsat S. , Babak S. , Baker J. , Dal Canton T. . Parameter estimation of stellar-mass black hole binaries with LISA. Phys. Rev. D, 2020, 102(12): 124037 https://doi.org/10.1103/PhysRevD.102.124037
56	Bartolo N. , Bertacca D. , Caldwell R. , R. Contaldi C. , Cusin G. . et al.. Probing anisotropies of the stochastic gravitational wave background with LISA. J. Cosmol. Astropart. Phys., 2022, 11: 009 https://doi.org/10.1088/1475-7516/2022/11/009
57	Abbott R. . et al.. (LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration), Upper limits on the isotropic gravitational-wave background from advanced LIGO and advanced Virgo’s third observing run. Phys. Rev. D, 2021, 104(2): 022004 https://doi.org/10.1103/PhysRevD.104.022004
58	LIGO Scientific Collaboration TheVirgo Collaboration theKAGRA Collaboration the, All-sky, all-frequency directional search for persistent gravitational waves from advanced LIGO’s and advanced Virgo’s first three observing runs, Phys. Rev. D 105(12), 122001 (2022)
59	Caprini C. , G. Figueroa D. , Flauger R. , Nardini G. , Peloso M. , Pieroni M. , Ricciardone A. , Tasinato G. . Reconstructing the spectral shape of a stochastic gravitational wave background with LISA. J. Cosmol. Astropart. Phys., 2019, 11: 017 https://doi.org/10.1088/1475-7516/2019/11/017
60	Hindmarsh M. , J. Huber S. , Rummukainen K. , J. Weir D. . Gravitational waves from the sound of a first order phase transition. Phys. Rev. Lett., 2014, 112(4): 041301 https://doi.org/10.1103/PhysRevLett.112.041301
61	Liu J. , K. Guo Z. , G. Cai R. , Shiu G. . Gravitational waves from oscillons with Cuspy potentials. Phys. Rev. Lett., 2018, 120(3): 031301 https://doi.org/10.1103/PhysRevLett.120.031301
62	G. Cai R. , Pi S. , Sasaki M. . Gravitational waves induced by non-Gaussian scalar perturbations. Phys. Rev. Lett., 2019, 122(20): 201101 https://doi.org/10.1103/PhysRevLett.122.201101
63	Yuan C. , G. Huang Q. . Gravitational waves induced by the local-type non-Gaussian curvature perturbations. Phys. Lett. B, 2021, 821: 136606 https://doi.org/10.1016/j.physletb.2021.136606
64	Falxa M. , Babak S. , Le Jeune M. . Adaptive kernel density estimation proposal in gravitational wave data analysis. Phys. Rev. D, 2023, 107(2): 022008 https://doi.org/10.1103/PhysRevD.107.022008

[1]	Meng-Qin Jiang, Nan Yang, Jin Li. Identify real gravitational wave events in the LIGO-Virgo catalog GWTC-1 and GWTC-2 with convolutional neural network[J]. Front. Phys. , 2022, 17(5): 54501-.
[2]	Xiang-Ru Li, Wo-Liang Yu, Xi-Long Fan, G. Jogesh Babu. Some optimizations on detecting gravitational wave using convolutional neural network[J]. Front. Phys. , 2020, 15(5): 54501-.
[3]	Bai-Jiong Lin, Xiang-Ru Li, Wo-Liang Yu. Binary neutron stars gravitational wave detection based on wavelet packet analysis and convolutional neural networks[J]. Front. Phys. , 2020, 15(2): 24602-.
[4]	He Gao, Shun-Ke Ai, Zhou-Jian Cao, Bing Zhang, Zhen-Yu Zhu, Ang Li, Nai-Bo Zhang, Andreas Bauswein. Relation between gravitational mass and baryonic mass for non-rotating and rapidly rotating neutron stars[J]. Front. Phys. , 2020, 15(2): 24603-.
[5]	Hua-Mei Luo, Wenbin Lin, Zu-Cheng Chen, Qing-Guo Huang. Extraction of gravitational wave signals with optimized convolutional neural network[J]. Front. Phys. , 2020, 15(1): 14601-.
[6]	Bing Zhang. The delay time of gravitational wave – gamma-ray burst associations[J]. Front. Phys. , 2019, 14(6): 64402-.

Viewed

Full text

Abstract

Cited

Shared

Discussed