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Mock data study for next-generation ground-based detectors: The performance loss of matched filtering due to correlated confusion noise (2209.03135v3)

Published 7 Sep 2022 in astro-ph.IM, astro-ph.HE, and gr-qc

Abstract: The next-generation (3G/XG) ground-based gravitational-wave (GW) detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE) will begin observing in the next decade. Due to the extremely high sensitivity of these detectors, the majority of stellar-mass compact-binary mergers in the entire Universe will be observed. It is also expected that 3G detectors will have significant sensitivity down to 2-7 Hz; the observed duration of binary neutron star signals could increase to several hours or days. The abundance and duration of signals will cause them to overlap in time, which may form a confusion noise that could affect the detection of individual GW sources when using naive matched filtering; matched filtering is only optimal for stationary Gaussian noise. We create mock data for CE and ET using the latest population models informed by the GWTC-3 catalog and investigate the performance loss of matched filtering due to overlapping signals. We find the performance loss mainly comes from a deviation in the noise's measured amplitude spectral density. The redshift reach of CE (ET) can be reduced by 15%-38% (8%-21%) depending on the merger rate estimate. The direct contribution of confusion noise to the total signal-to-noise ratio (SNR) is generally negligible compared to the contribution from instrumental noise. We also find that correlated confusion noise has a negligible effect on the quadrature summation rule of network SNR for ET, but might reduce the network SNR of high detector-frame mass signals for detector networks including CE if no mitigation is applied. For ET, the null stream can mitigate the astrophysical foreground. For CE, we demonstrate that a computationally efficient, straightforward single-detector signal subtraction method suppresses the total noise to almost the instrument noise level; this will allow for near-optimal searches.

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References (107)
  1. B. P. Abbott et al. (LIGO Scientific, Virgo), Observation of Gravitational Waves from a Binary Black Hole Merger, Phys. Rev. Lett. 116, 061102 (2016), arXiv:1602.03837 [gr-qc] .
  2. J. R. Smith (LIGO Scientific), The Path to the enhanced and advanced LIGO gravitational-wave detectors, Class. Quant. Grav. 26, 114013 (2009), arXiv:0902.0381 [gr-qc] .
  3. T. Accadia et al., Status of the Virgo project, Class. Quant. Grav. 28, 114002 (2011).
  4. F. Acernese et al. (Virgo), Increasing the Astrophysical Reach of the Advanced Virgo Detector via the Application of Squeezed Vacuum States of Light, Phys. Rev. Lett. 123, 231108 (2019).
  5. L. McCuller et al., Frequency-Dependent Squeezing for Advanced LIGO, Phys. Rev. Lett. 124, 171102 (2020), arXiv:2003.13443 [astro-ph.IM] .
  6. B. P. Abbott et al. (LIGO Scientific, Virgo), GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral, Phys. Rev. Lett. 119, 161101 (2017a), arXiv:1710.05832 [gr-qc] .
  7. B. P. Abbott et al. (LIGO Scientific, Virgo), GW190425: Observation of a Compact Binary Coalescence with Total Mass ∼3.4⁢M⊙similar-toabsent3.4subscript𝑀direct-product\sim 3.4M_{\odot}∼ 3.4 italic_M start_POSTSUBSCRIPT ⊙ end_POSTSUBSCRIPT, Astrophys. J. Lett. 892, L3 (2020), arXiv:2001.01761 [astro-ph.HE] .
  8. B. P. Abbott et al. (LIGO Scientific, Virgo), Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo, Astrophys. J. Lett. 882, L24 (2019a), arXiv:1811.12940 [astro-ph.HE] .
  9. R. Abbott et al. (LIGO Scientific, Virgo), Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog, Astrophys. J. Lett. 913, L7 (2021c), arXiv:2010.14533 [astro-ph.HE] .
  10. H. Abe et al. (KAGRA), Performance of the KAGRA detector during the first joint observation with GEO 600 (O3GK),   (2022), arXiv:2203.07011 [astro-ph.IM] .
  11. B. Willke et al., The GEO 600 gravitational wave detector, Class. Quant. Grav. 19, 1377 (2002).
  12. M. Saleem et al., The science case for LIGO-India, Class. Quant. Grav. 39, 025004 (2022), arXiv:2105.01716 [gr-qc] .
  13. C. Cahillane and G. Mansell, Review of the Advanced LIGO Gravitational Wave Observatories Leading to Observing Run Four, Galaxies 10, 36 (2022), arXiv:2202.00847 [gr-qc] .
  14. Y. Michimura et al., Prospects for improving the sensitivity of the cryogenic gravitational wave detector KAGRA, Phys. Rev. D 102, 022008 (2020), arXiv:2006.08970 [gr-qc] .
  15. M. Punturo et al., The Einstein Telescope: A third-generation gravitational wave observatory, Class. Quant. Grav. 27, 194002 (2010a).
  16. M. Punturo et al., The third generation of gravitational wave observatories and their science reach, Class. Quant. Grav. 27, 084007 (2010b).
  17. S. Hild et al., Sensitivity Studies for Third-Generation Gravitational Wave Observatories, Class. Quant. Grav. 28, 094013 (2011), arXiv:1012.0908 [gr-qc] .
  18. M. Maggiore et al., Science Case for the Einstein Telescope, JCAP 03, 050, arXiv:1912.02622 [astro-ph.CO] .
  19. S. Di Pace et al., Research Facilities for Europe’s Next Generation Gravitational-Wave Detector Einstein Telescope, Galaxies 10, 10.3390/galaxies10030065 (2022).
  20. B. P. Abbott et al. (LIGO Scientific), Exploring the Sensitivity of Next Generation Gravitational Wave Detectors, Class. Quant. Grav. 34, 044001 (2017b), arXiv:1607.08697 [astro-ph.IM] .
  21. D. Reitze et al., Cosmic Explorer: The U.S. Contribution to Gravitational-Wave Astronomy beyond LIGO, Bull. Am. Astron. Soc. 51, 035 (2019), arXiv:1907.04833 [astro-ph.IM] .
  22. P. Amaro-Seoane et al. (LISA), Laser Interferometer Space Antenna,   (2017), arXiv:1702.00786 [astro-ph.IM] .
  23. J. Baker et al., The Laser Interferometer Space Antenna: Unveiling the Millihertz Gravitational Wave Sky,   (2019), arXiv:1907.06482 [astro-ph.IM] .
  24. X.-F. Gong et al., A scientific case study of an advanced LISA mission, Class. Quant. Grav. 28, 094012 (2011).
  25. W.-R. Hu and Y.-L. Wu, The Taiji Program in Space for gravitational wave physics and the nature of gravity, Natl. Sci. Rev. 4, 685 (2017).
  26. Y.-L. Wu et al., Taiji program in space for gravitational universe with the first run key technologies test in Taiji-1, Int. J. Mod. Phys. A 36, 2102002 (2021).
  27. J. Luo et al. (TianQin), TianQin: a space-borne gravitational wave detector, Class. Quant. Grav. 33, 035010 (2016), arXiv:1512.02076 [astro-ph.IM] .
  28. Y.-M. Hu, J. Mei, and J. Luo, Science prospects for space-borne gravitational-wave missions, Natl. Sci. Rev. 4, 683 (2017).
  29. J. Mei et al. (TianQin), The TianQin project: current progress on science and technology, PTEP 2021, 05A107 (2021), arXiv:2008.10332 [gr-qc] .
  30. J. Luo et al., The first round result from the TianQin-1 satellite, Class. Quant. Grav. 37, 185013 (2020), arXiv:2008.09534 [physics.ins-det] .
  31. J.-P. Zhu et al., Kilonova Emission from Black Hole–Neutron Star Mergers. II. Luminosity Function and Implications for Target-of-opportunity Observations of Gravitational-wave Triggers and Blind Searches, Astrophys. J. 917, 24 (2021a), arXiv:2011.02717 [astro-ph.HE] .
  32. G. Nelemans, L. R. Yungelson, and S. F. Portegies Zwart, The gravitational wave signal from the galactic disk population of binaries containing two compact objects, Astron. Astrophys. 375, 890 (2001), arXiv:astro-ph/0105221 .
  33. S. E. Timpano, L. J. Rubbo, and N. J. Cornish, Characterizing the galactic gravitational wave background with LISA, Phys. Rev. D 73, 122001 (2006), arXiv:gr-qc/0504071 .
  34. J. Crowder and N. Cornish, A Solution to the Galactic Foreground Problem for LISA, Phys. Rev. D 75, 043008 (2007), arXiv:astro-ph/0611546 .
  35. M. C. Digman and N. J. Cornish, LISA Gravitational Wave Sources in A Time-Varying Galactic Stochastic Background,   (2022), arXiv:2206.14813 [astro-ph.IM] .
  36. G. Turin, An introduction to matched filters, IRE transactions on Information theory 6, 311 (1960).
  37. M. Cabero et al., Blip glitches in Advanced LIGO data, Class. Quant. Grav. 36, 15 (2019), arXiv:1901.05093 [physics.ins-det] .
  38. D. Davis et al. (LIGO), LIGO detector characterization in the second and third observing runs, Class. Quant. Grav. 38, 135014 (2021), arXiv:2101.11673 [astro-ph.IM] .
  39. B. J. Owen and B. S. Sathyaprakash, Matched filtering of gravitational waves from inspiraling compact binaries: Computational cost and template placement, Phys. Rev. D 60, 022002 (1999), arXiv:gr-qc/9808076 .
  40. S. A. Usman et al., The PyCBC search for gravitational waves from compact binary coalescence, Class. Quant. Grav. 33, 215004 (2016), arXiv:1508.02357 [gr-qc] .
  41. T. Regimbau and S. A. Hughes, Gravitational-wave confusion background from cosmological compact binaries: Implications for future terrestrial detectors, Phys. Rev. D 79, 062002 (2009), arXiv:0901.2958 [gr-qc] .
  42. T. Regimbau et al., A Mock Data Challenge for the Einstein Gravitational-Wave Telescope, Phys. Rev. D 86, 122001 (2012), arXiv:1201.3563 [gr-qc] .
  43. D. A. Brown (LIGO Scientific), Using the INSPIRAL program to search for gravitational waves from low-mass binary inspiral, Class. Quant. Grav. 22, S1097 (2005), arXiv:gr-qc/0505102 .
  44. S. Babak et al., Searching for gravitational waves from binary coalescence, Phys. Rev. D 87, 024033 (2013), arXiv:1208.3491 [gr-qc] .
  45. Y. Guersel and M. Tinto, Near optimal solution to the inverse problem for gravitational wave bursts, Phys. Rev. D 40, 3884 (1989).
  46. L. Wen and B. F. Schutz, Coherent network detection of gravitational waves: The Redundancy veto, Class. Quant. Grav. 22, S1321 (2005), arXiv:gr-qc/0508042 .
  47. P. Ajith, M. Hewitson, and I. S. Heng, Null-stream veto for two co-located detectors: Implementation issues, Class. Quant. Grav. 23, S741 (2006), arXiv:gr-qc/0604004 .
  48. L. Wen, Data analysis of gravitational waves using a network of detectors, Int. J. Mod. Phys. D 17, 1095 (2008), arXiv:gr-qc/0702096 .
  49. W. Dupree and S. Bose, Multi-detector null-stream-based x2superscript𝑥2x^{2}italic_x start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT statistic for compact binary coalescence searches, Class. Quant. Grav. 36, 195012 (2019), arXiv:1912.11667 [gr-qc] .
  50. I. C. F. Wong and T. G. F. Li, Signal space in the triangular network of the Einstein Telescope, Phys. Rev. D 105, 084002 (2022), arXiv:2108.05108 [gr-qc] .
  51. B. Goncharov, A. H. Nitz, and J. Harms, Utilizing the null stream of the Einstein Telescope, Phys. Rev. D 105, 122007 (2022), arXiv:2204.08533 [gr-qc] .
  52. R. Smith et al., Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories, Phys. Rev. Lett. 127, 081102 (2021), arXiv:2103.12274 [gr-qc] .
  53. P. Relton and V. Raymond, Parameter estimation bias from overlapping binary black hole events in second generation interferometers, Phys. Rev. D 104, 084039 (2021), arXiv:2103.16225 [gr-qc] .
  54. Y. Himemoto, A. Nishizawa, and A. Taruya, Impacts of overlapping gravitational-wave signals on the parameter estimation: Toward the search for cosmological backgrounds, Phys. Rev. D 104, 044010 (2021), arXiv:2103.14816 [gr-qc] .
  55. A. Antonelli, O. Burke, and J. R. Gair, Noisy neighbours: inference biases from overlapping gravitational-wave signals, Mon. Not. Roy. Astron. Soc. 507, 5069 (2021), arXiv:2104.01897 [gr-qc] .
  56. E. E. Flanagan and T. Hinderer, Constraining neutron star tidal Love numbers with gravitational wave detectors, Phys. Rev. D 77, 021502 (2008), arXiv:0709.1915 [astro-ph] .
  57. M. Favata, Systematic parameter errors in inspiraling neutron star binaries, Phys. Rev. Lett. 112, 101101 (2014), arXiv:1310.8288 [gr-qc] .
  58. S. S. Bavera et al., The impact of mass-transfer physics on the observable properties of field binary black hole populations, Astron. Astrophys. 647, A153 (2021), arXiv:2010.16333 [astro-ph.HE] .
  59. G. Fragione, N. Leigh, and R. Perna, Black hole and neutron star mergers in Galactic Nuclei: the role of triples, Mon. Not. Roy. Astron. Soc. 488, 2825 (2019), arXiv:1903.09160 [astro-ph.GA] .
  60. B. Carr, F. Kuhnel, and M. Sandstad, Primordial Black Holes as Dark Matter, Phys. Rev. D 94, 083504 (2016), arXiv:1607.06077 [astro-ph.CO] .
  61. P. Madau and M. Dickinson, Cosmic Star Formation History, Ann. Rev. Astron. Astrophys. 52, 415 (2014), arXiv:1403.0007 [astro-ph.CO] .
  62. P. A. R. Ade et al. (Planck), Planck 2015 results. XIII. Cosmological parameters, Astron. Astrophys. 594, A13 (2016), arXiv:1502.01589 [astro-ph.CO] .
  63. D. Wanderman and T. Piran, The rate, luminosity function and time delay of non-Collapsar short GRBs, Mon. Not. Roy. Astron. Soc. 448, 3026 (2015), arXiv:1405.5878 [astro-ph.HE] .
  64. S. Ando, Short gamma-ray bursts as a possible probe of binary neutron star mergers, JCAP 06, 007, arXiv:astro-ph/0405411 .
  65. R. W. O’Shaughnessy, V. Kalogera, and K. Belczynski, Short Gamma-Ray Bursts and Binary Mergers in Spiral and Elliptical Galaxies: Redshift Distribution and Hosts, Astrophys. J. 675, 566 (2008), arXiv:0706.4139 [astro-ph] .
  66. Webinar: The population of merging compact binaries inferred using GWs through GWTC-3 https://dcc.ligo.org/LIGO-G2102458/public.
  67. A. Matas et al., Aligned-spin neutron-star–black-hole waveform model based on the effective-one-body approach and numerical-relativity simulations, Phys. Rev. D 102, 043023 (2020), arXiv:2004.10001 [gr-qc] .
  68. F. Acernese et al. (LIGO, Virgo), Status of Advanced Virgo, EPJ Web Conf. 182, 02003 (2018).
  69. LIGO Scientific Collaboration, LIGO Algorithm Library - LALSuite, free software (GPL) (2018).
  70. W. Zhao and L. Wen, Localization accuracy of compact binary coalescences detected by the third-generation gravitational-wave detectors and implication for cosmology, Phys. Rev. D 97, 064031 (2018), arXiv:1710.05325 [astro-ph.CO] .
  71. S. Borhanian and B. S. Sathyaprakash, Listening to the Universe with Next Generation Ground-Based Gravitational-Wave Detectors,   (2022), arXiv:2202.11048 [gr-qc] .
  72. T. W. Baumgarte and S. L. Shapiro, Numerical Relativity: Solving Einstein’s Equations on the Computer (Cambridge University Press, 2010).
  73. T. W. Baumgarte and S. L. Shapiro, Numerical Relativity: Starting from Scratch (Cambridge University Press, 2021).
  74. G. Pratten et al., Computationally efficient models for the dominant and subdominant harmonic modes of precessing binary black holes, Phys. Rev. D 103, 104056 (2021), arXiv:2004.06503 [gr-qc] .
  75. A. Buonanno and T. Damour, Transition from inspiral to plunge in binary black hole coalescences, Phys. Rev. D 62, 064015 (2000), arXiv:gr-qc/0001013 .
  76. S. Ossokine et al., Multipolar Effective-One-Body Waveforms for Precessing Binary Black Holes: Construction and Validation, Phys. Rev. D 102, 044055 (2020), arXiv:2004.09442 [gr-qc] .
  77. M. Maggiore, Gravitational Waves. Vol. 1: Theory and Experiments, Oxford Master Series in Physics (Oxford University Press, 2007).
  78. P. Welch, The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms, IEEE Transactions on audio and electroacoustics 15, 70 (1967).
  79. K. Chatziioannou and W. M. Farr, Inferring the maximum and minimum mass of merging neutron stars with gravitational waves, Phys. Rev. D 102, 064063 (2020), arXiv:2005.00482 [astro-ph.HE] .
  80. B. P. Abbott et al. (LIGO Scientific, Virgo), Search for Subsolar Mass Ultracompact Binaries in Advanced LIGO’s Second Observing Run, Phys. Rev. Lett. 123, 161102 (2019b), arXiv:1904.08976 [astro-ph.CO] .
  81. A. H. Nitz and Y.-F. Wang, Search for Gravitational Waves from the Coalescence of Subsolar-Mass Binaries in the First Half of Advanced LIGO and Virgo’s Third Observing Run, Phys. Rev. Lett. 127, 151101 (2021), arXiv:2106.08979 [astro-ph.HE] .
  82. S. M. Koushiappas and A. Loeb, Maximum redshift of gravitational wave merger events, Phys. Rev. Lett. 119, 221104 (2017), arXiv:1708.07380 [astro-ph.CO] .
  83. C. Cutler and E. E. Flanagan, Gravitational waves from merging compact binaries: How accurately can one extract the binary’s parameters from the inspiral wave form?, Phys. Rev. D 49, 2658 (1994), arXiv:gr-qc/9402014 .
  84. L. S. Finn, Aperture synthesis for gravitational wave data analysis: Deterministic sources, Phys. Rev. D 63, 102001 (2001), arXiv:gr-qc/0010033 .
  85. N. Seto, Correlation of gravitational wave background noises and statistical loss for angular averaged sensitivity curves, Phys. Rev. D 104, 063025 (2021), arXiv:2108.12930 [gr-qc] .
  86. N. V. Fotopoulos (LIGO Scientific), Searching for stochastic gravitational-wave background with the co-located LIGO interferometers, J. Phys. Conf. Ser. 122, 012032 (2008), arXiv:0801.3429 [gr-qc] .
  87. J. Aasi et al. (LIGO Scientific, VIRGO), Searching for stochastic gravitational waves using data from the two colocated LIGO Hanford detectors, Phys. Rev. D 91, 022003 (2015), arXiv:1410.6211 [gr-qc] .
  88. C. Cutler and J. Harms, BBO and the neutron-star-binary subtraction problem, Phys. Rev. D 73, 042001 (2006), arXiv:gr-qc/0511092 .
  89. A. Sharma and J. Harms, Searching for cosmological gravitational-wave backgrounds with third-generation detectors in the presence of an astrophysical foreground, Phys. Rev. D 102, 063009 (2020), arXiv:2006.16116 [gr-qc] .
  90. S. Sachdev, T. Regimbau, and B. S. Sathyaprakash, Subtracting compact binary foreground sources to reveal primordial gravitational-wave backgrounds, Phys. Rev. D 102, 024051 (2020), arXiv:2002.05365 [gr-qc] .
  91. S. Babak, Building a stochastic template bank for detecting massive black hole binaries, Class. Quant. Grav. 25, 195011 (2008), arXiv:0801.4070 [gr-qc] .
  92. C. Messenger, R. Prix, and M. A. Papa, Random template banks and relaxed lattice coverings, Phys. Rev. D 79, 104017 (2009), arXiv:0809.5223 [gr-qc] .
  93. I. W. Harry, B. Allen, and B. S. Sathyaprakash, A Stochastic template placement algorithm for gravitational wave data analysis, Phys. Rev. D 80, 104014 (2009), arXiv:0908.2090 [gr-qc] .
  94. G. M. Manca and M. Vallisneri, Cover art: Issues in the metric-guided and metric-less placement of random and stochastic template banks, Phys. Rev. D 81, 024004 (2010), arXiv:0909.0563 [gr-qc] .
  95. B. Allen, Performance of random template banks, Phys. Rev. D 105, 102003 (2022), arXiv:2203.02759 [gr-qc] .
  96. B. J. Owen, Search templates for gravitational waves from inspiraling binaries: Choice of template spacing, Phys. Rev. D 53, 6749 (1996), arXiv:gr-qc/9511032 .
  97. T. Cokelaer, Gravitational waves from inspiralling compact binaries: Hexagonal template placement and its efficiency in detecting physical signals, Phys. Rev. D 76, 102004 (2007), arXiv:0706.4437 [gr-qc] .
  98. S. Roy, A. S. Sengupta, and P. Ajith, Effectual template banks for upcoming compact binary searches in Advanced-LIGO and Virgo data, Phys. Rev. D 99, 024048 (2019), arXiv:1711.08743 [gr-qc] .
  99. T. Dal Canton and I. W. Harry, Designing a template bank to observe compact binary coalescences in Advanced LIGO’s second observing run,   (2017), arXiv:1705.01845 [gr-qc] .
  100. S. Roy, A. S. Sengupta, and N. Thakor, Hybrid geometric-random template-placement algorithm for gravitational wave searches from compact binary coalescences, Phys. Rev. D 95, 104045 (2017), arXiv:1702.06771 [gr-qc] .
  101. L. S. Finn, Detection, measurement and gravitational radiation, Phys. Rev. D 46, 5236 (1992), arXiv:gr-qc/9209010 .
  102. A. H. Nitz and T. Dal Canton, Pre-merger Localization of Compact-binary Mergers with Third-generation Observatories, Astrophys. J. Lett. 917, L27 (2021), arXiv:2106.15259 [astro-ph.HE] .
  103. G. Van Rossum and F. L. Drake Jr, Python tutorial, Vol. 620 (Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands, 1995).
  104. C. R. Harris et al., Array programming with NumPy, Nature 585, 357 (2020), arXiv:2006.10256 [cs.MS] .
  105. A. Meurer et al., SymPy: symbolic computing in Python, PeerJ Comput. Sci. 3, e103 (2017).
  106. P. Virtanen et al., SciPy 1.0–Fundamental Algorithms for Scientific Computing in Python, Nature Meth. 17, 261 (2020), arXiv:1907.10121 [cs.MS] .
  107. J. D. Hunter, Matplotlib: A 2d graphics environment, Computing in Science & Engineering 9, 90 (2007).
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