Overview of KAGRA Data Analysis Hideyuki Tagoshi for the KAGRA - - PowerPoint PPT Presentation

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Overview of KAGRA Data Analysis Hideyuki Tagoshi for the KAGRA - - PowerPoint PPT Presentation

May 13, 2023 286 likes •461 views

Overview of KAGRA Data Analysis Hideyuki Tagoshi for the KAGRA Collaboration JGW-G1910798 1 TAUP2019, 2019/09/10, Toyama Anticipated observation schedule Fig. 2 to be updated 2019/4/20 LVK observation scenario 8 2 Overview of KAGRA data

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SLIDE 1

Overview of KAGRA Data Analysis

Hideyuki Tagoshi for the KAGRA Collaboration

1 TAUP2019, 2019/09/10, Toyama JGW-G1910798
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SLIDE 2

Anticipated observation schedule

2
  • Fig. 2 to be updated
2019/4/20 LVK observation scenario 8
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SLIDE 3

Overview of KAGRA data analysis

3 KAGRA is planning to perform the first observation during FY2019. This is done together with LIGO-Virgo O3. New MOU between LIGO-Virgo and KAGRA are now prepared in order to perform joint observation and joint data analysis. A lot of efforts are necessary to perform start this observation in data analysis KAGRA data will be shared with LIGO-Virgo, and will be jointly analyzed. LIGO-Virgo are conducting data analysis completely jointly. We have to establish an organization for this joint data analysis. KAGRA data analysis group was reformulated in order to have a good matching with corresponding LIGO-Virgo data analysis working groups
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SLIDE 4

KAGRA Data Analysis Working Group

4 PI Executive Office KSC board System Engineering Office Subsystem 1 Subsystem 2 Subsystem 3 Subsystem n Data Analysis Committee CBC Burst Continuous Waves Stochastic background Detector Characterization Calibration Data Analysis Working Group (DAWG) Computing and Software
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SLIDE 5

4 Source Categories

5 Compact Binary Coalescence (CBC) Burst Continuous Waves (CW) Stochastic Background
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SLIDE 6

Source Categories

6

Individual sources

known waveform unknown waveform Short duration Compact Binary Coalescenses Supernovae pulsar glitches … Long duration Pulsar (rotating NS)

Stochastic background

Early universe origin Astrophysical origin inflation ... Superposition of many sources Nearly isotropic anisotropic nearly isotropic
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SLIDE 7

Maximum likelihood ratio test

7

x(t) = n(t) + s(t)

(data)=(noise)+(signal)

Λm = max

i

 p(x|Hi) p(x|H0)

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H0: Null hypothesis Hi: Alternative hypothesis
  • Known waveform cases (CBC, Continuous waves)
The form of s(t, θi ) is known, but depends on parameters, θi => Matched filtering
  • Unknown waveform cases (Burst)
s(t) is unknown. The waveform si = s(ti) themselves are unknown parameters => Coherent network analysis (for multiple detectors) Excess power statistics (for one detector case)
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SLIDE 8

Stochastic background

8 Cross correlation analysis Difficult to distinguish between noise and signal with a single detector. But different detectors should respond in a similar way. 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sha1_base64="FzMepq68rJErMihdgxILcjA0lsE=">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</latexit><latexit 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sha1_base64="FzMepq68rJErMihdgxILcjA0lsE=">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</latexit><latexit sha1_base64="FzMepq68rJErMihdgxILcjA0lsE=">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</latexit> Γ(f) <latexit sha1_base64="cQ7wuU95PrmRhx/aJXUsTqdbfp0=">ACbXichVHLSgMxFD0dX7U+WhVBUKRYfG1KoLiquhCl63aVnwgM2OqofNiJi3U4g+4FlyIgoKI+Blu/AEXfoK4cKHgxoW30wFRUW9IcnJyz81JojmG8CRjDyGlqbmltS3cHuno7OqOxnp685dnWe023Ddtc01eOGsHhOCmnwNcflqkZvKCVFur7hQp3PWFbq7Lq8C1T3bVEUeiqJGp9c1E1TXWiOBnfjiVYkvkR/wlSAUgiIwdu8ImdmBDRxkmOCxIwgZUeNQ2kAKDQ9wWasS5hIS/z3GACGnLlMUpQyW2ROMurTYC1qJ1vabnq3U6xaDukjKOUXbPrtkLu2M37JG9/1qr5teoe6nSrDW03NmOHg6svP2rMmW2PtU/elZohZ36sg747P1G+hN/SV/eOXlbnl0doYu2BP5P+cPbBbuoFVedUvs3z5BH6gNT35/4J8lPJFOHsdCI9H3xFGIMYwQS9wzSWEIGOTrXwhFOcRZ6VvqVIW4kaqEAk0fvoQy/gGTGozw</latexit><latexit sha1_base64="cQ7wuU95PrmRhx/aJXUsTqdbfp0=">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</latexit><latexit sha1_base64="cQ7wuU95PrmRhx/aJXUsTqdbfp0=">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</latexit><latexit sha1_base64="cQ7wuU95PrmRhx/aJXUsTqdbfp0=">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</latexit> : overlap reduction function

˜ xi(f)

<latexit sha1_base64="8Hy/ePIWC4zsPkjA+Li5thWH6ig=">ACdHichVHLTsJAFD3UF+ID1I2JLogEoxsyGBONK6Ibl4AiJGBIWwedUNqmHYhI+AF/wAUrjcQYP8ONP+DCTzAuNbpx4aU0MWrU20znzJl7pyZq9mGcCVjDwFlYHBoeCQ4Ghobn5gMR6am91yr7ug8p1uG5RQ01eWGMHlOCmnwgu1wtaYZPK9Vt3r7+QZ3XGZu7Jp8/2aemiKitBVSVQ5Ei5JYRzw1nG7LJYqy9FyJMYSzIvoT5D0Qx+pK3IFUo4gAUdTAYUISNqDCpa+IJBhs4vbRIs4hJLx9jZCpK1TFqcMldgq/Q9pVfRZk9a9mq6n1ukUg4ZDyiji7J5ds2d2x27YI3v/tVbLq9Hz0qRZ62u5XQ6fzu68/auq0Sx9Kn607NEBeueV0HebY/p3ULv6xsnZ87G9l4a5FdsCfyf84e2C3dwGy86N0Mz3YQogYkvz/3T7C3kgSzqzGUpt+K4KYwKW6L3XkMI20sh5PengEt3AqzKvxJR4P1UJ+JoZfAkl8QG+q4+6</latexit><latexit sha1_base64="8Hy/ePIWC4zsPkjA+Li5thWH6ig=">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</latexit><latexit sha1_base64="8Hy/ePIWC4zsPkjA+Li5thWH6ig=">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</latexit><latexit sha1_base64="8Hy/ePIWC4zsPkjA+Li5thWH6ig=">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</latexit> : data of i-th detector
  • Isotropic search
  • Directional search => Talk by Jishnu Suresh
  • Phys. Rev. D 100, 062001 (2019)
slide-9
SLIDE 9

Analysis tasks for O3

9
  • 1. Search pipelines
  • 4-detectors analysis (LIGO-Hanford, LIGO-Livingston, Virgo, KAGRA)
ü Not only data themselves, we also need to provide necessary information for LIGO-Virgo in order to analyze KAGRA data (lock flag, data quality, ...)
  • LIGO-Virgo CBC pipelines (gstlal, pyCBC, MBTA, SPIIR)
  • gstlal => Univ. Tokyo LIGO/KAGRA group
  • LIGO-Virgo Burst pipelines (coherent WaveBurst)
  • new analysis to extract properties of core-collapse supernovae
  • KAGRA CBC pipeline (under development)
  • KAGRA Continuous wave search efforts for known pulsar
  • Stochastic background search (directional, isotropic)
slide-10
SLIDE 10

Parameter estimation

10 Event search => Candidate events with best fit parameters To obtain estimate of parameter estimation error => Bayesian methods are used to evaluate posterior distribution
  • f parameters
(sky location, mass of BH, ...) 2 major methods:
  • MCMC (Markov chain Monte Carlo) with Metropolis-Hastings
algorithm
  • Nested sampling
slide-11
SLIDE 11

Analysis tasks for O3

11
  • 2. Parameter estimation
For KAGRA at O3, parameter estimation would be more important than search tasks because of low sensitivities of KAGRA. KAGRA data might be only effective to improve sky localization accuracy. KAGRA's efforts
  • MCMC (MH) pipeline
  • GPU accelarated Nested sampling
Parameter estimation rota for LIGO-Virgo-KAGRA O3b
slide-12
SLIDE 12

Other projects

12
  • 1. Testing specific theories of gravity by using detected CBC signals
Ref: Testing massive-field modifications of gravity via gravitational waves, arXiv:1905.11859
  • 2. Search for echos of BBH
Ref: Searching for black hole echoes from the LIGO-Virgo Catalog GWTC-1, arXiv:1906.00838
  • 3. Extraction of frequency and dumping time from QNM part of BBH signals with several
new methods Ref: Nakano et al, PRD 99 (2019) 124032
  • 4. Constraining tidal deformability of neutron stars
Ref: Discrepancy in tidal deformability of GW170817 between the advanced LIGO twins arXiv:1812.06100v2 ...
slide-13
SLIDE 13

Impcat of KAGRA at O3

13

Circulation

Assumed sensitivity (BNS range) KAGRA 25Mpc, 15Mpc, 8Mpc LIGO 120Mpc, Virgo 60Mpc Observing Senario Paper, Fig. 1
slide-14
SLIDE 14 14 Assumed sensitivity (BNS range) LIGO 120Mpc, Virgo 60Mpc, KAGRA 25Mpc

Source localization

LIGO-Virgo 2 detectors LIGO-Virgo-KAGRA 3 detectors LIGO-Virgo 3 detectors LIGO-Virgo-KAGRA 4 detectors
slide-15
SLIDE 15

Impcat of KAGRA at O3: 2 detectors v.s. 3 detectors

15 (a) KAGRA’s BNS range: 25 Mpc Source HL HLK HV HVK LV LVK BBH 701 434 371 77.5 302 112 NSBH 788 470 302 74.3 397 158 BNS 508 323 268 75.4 215 84.5 (b) KAGRA’s BNS range: 15 Mpc Source HL HLK HV HVK LV LVK BBH 701 599 371 207 302 207 NSBH 788 651 302 168 397 250 BNS 508 428 268 151 215 157 (c) KAGRA’s BNS range: 8 Mpc Source HL HLK HV HVK LV LVK BBH 701 676 371 306 302 273 NSBH 788 742 302 244 397 342 BNS 508 484 268 254 215 191 Table 2: The median of the distribution of 90%-credible region of the source localization for LIGO-Virgo 2-detectors network, and 3-detectors network including KAGRA. All values are in the unit of deg2. LVK Taskforce for KAGRA O3 especially Y.J. Huang, Sadakazu Haino, C.Y. Lin Preliminary
slide-16
SLIDE 16

Appendix

16