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Characterization of H ardware I njections in LIGO D ata

Characterization of H ardware I njections in LIGO D ata. By Shannon Wang Mentors: Alan Weinstein, Jonah Kanner. Objectives. Data from S6 run contains simulated signals from compact binary coalescences (CBCs) that were injected into the H1 and L1 detectors.

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Characterization of H ardware I njections in LIGO D ata

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  1. Characterization of Hardware Injections in LIGO Data By Shannon Wang Mentors: Alan Weinstein, Jonah Kanner

  2. Objectives • Data from S6 run contains simulated signals from compact binary coalescences (CBCs) that were injected into the H1 and L1 detectors. • Signals were produced by physically moving test masses. • We’re seeking to retrieve these signals through matched filtering. • The injection times and merger times of the injections are recorded, so the signal to noise ratios (SNRs) should be easily retrieved. LIGO Scientific Collaboration

  3. Compact Binary Coalescences Image from ligo.org • Three stages: inspiral, merger, ringdown • Three types: neutron star-neutron star (NSNS), neutron star-black hole (NSBH), black hole-black hole (BHBH) LIGO Scientific Collaboration

  4. Chirp Waveform • Signals appear in data as chirp waveforms. • Chirp waveforms are determined by the masses of the binaries. • NSNS have the longest waveforms, because they reach the merger phase at high frequencies. • BHBH have the shortest waveforms, because they reach the merger phase at low frequencies. LIGO Scientific Collaboration

  5. Distribution of Masses LIGO Scientific Collaboration

  6. Chirp Waveform (cont.) LIGO Scientific Collaboration

  7. Matched Filter LIGO Scientific Collaboration

  8. Power Spectral Density • Took average power spectrum • Took the mean of the power spectrum of eight segments • Segment defined as the length of the data that is later multiplied by the template LIGO Scientific Collaboration

  9. Hardware Injections • EOBNRpseudoFourPN: makes up the bulk of the injections. • GeneratePPNtwoPN: can be reasonably approximated using FindCHIRP template. • SpinTaylorT4threePointFivePN: has spin. LIGO Scientific Collaboration

  10. Final Approach • Used lalapp coinj to make the templates used to produce the injections. • Took 100 seconds of data because the templates are 100 seconds long. • Wrote script to identify the template using the injection time. LIGO Scientific Collaboration

  11. Lessons Learned • Neglected windowing – take care so that the merger is centered in the data. • Offset in recovered time = injection time + 100 – merger time • Templates already normalized to effective distance. • Edit xml files so that coinj works. LIGO Scientific Collaboration

  12. Recovered SNRs for L1 LIGO Scientific Collaboration

  13. Recovered SNRs for L1 LIGO Scientific Collaboration

  14. Unsuccessful Injections in L1 LIGO Scientific Collaboration

  15. Recovered SNRs for H1 LIGO Scientific Collaboration

  16. Recovered SNRs for H1 LIGO Scientific Collaboration

  17. Unsuccessful Injections in H1 LIGO Scientific Collaboration

  18. Discrepancies • Unexpected: possible signals when status is recorded as “Not in science mode,”“GRB alert,” and “Injection compromised.” • Anomalies: when the ratio of the recovered to the expected SNR is lower than 0.5 or higher than 2, and expected SNR is higher than 6. • Deceptive: injection marked as “Successful” but the recovered time differs greatly from the merger time. • Shorties: injections that don’t have 100 seconds of data. LIGO Scientific Collaboration

  19. Discrepancies • 85 injections that produced questionable results • Could plot Fourier-transformed data against frequency template and average power spectrum as sanity check • Could run omega scan on both merger and recovered times as sanity check LIGO Scientific Collaboration

  20. Accounting for Shorties • Still grabbed 100 seconds of data using getsegs from readligo.py. • If data missing from end of segment, zero-padded end. • If data missing from beginning of segment, zero-padded beginning. LIGO Scientific Collaboration

  21. Discrete Fourier Transform • The discrete Fourier transform approximates the continuous Fourier transform at frequency kΔf. • The nature of the transform is that the length of the template affects the outcome of the transform. LIGO Scientific Collaboration

  22. Successful Injection LIGO Scientific Collaboration

  23. Successful Injection LIGO Scientific Collaboration

  24. Unsuccessful Injection LIGO Scientific Collaboration

  25. Unsuccessful Injection LIGO Scientific Collaboration

  26. Omega Scan - Glitch LIGO Scientific Collaboration

  27. Omega Scan – Glitch LIGO Scientific Collaboration

  28. Omega Scan – Injection Present LIGO Scientific Collaboration

  29. Future Work • Currently we’re working with the lists H1biinjlist.txt and L1biinjlist.txt. • We need to ascertain that those two lists contain all of the hardware injections. • There are six injections in the H1biinjlist.txt that cannot be matched to any injection in the parameter files. • The burst injections also need to be retrieved. LIGO Scientific Collaboration

  30. Acknowledgements Thank you to… Jonah Kanner Alan Weinstein Tom Tombrello LIGO Scientific Collaboration

  31. Backup Slides LIGO Scientific Collaboration

  32. Chirp Waveform (cont.) • Plus and cross represent the two polarizations. • D is distance from source. • M is total mass; μ is reduced mass; η is reduced mass over total mass; slanted M M is chirp mass, or η3/5M. LIGO Scientific Collaboration

  33. FindCHIRP Approach LIGO Scientific Collaboration

  34. Problems Encountered LIGO Scientific Collaboration

  35. Initial Attempts • Tried running FINDChirp matched filter on S5 hardware injections. • Results were successful, which confirmed that the templates were correct. • Switched to third order Post-Newtonian approximation for frequency template. • Switched window from 4 seconds to 16 seconds and then 32 seconds LIGO Scientific Collaboration

  36. Initial Attempts (cont.) • Chirp times are the durations of signals that start at the lowest frequencies and end at the frequencies at which the systems coalesce LIGO Scientific Collaboration

  37. LALSuite Approach • First tried IMRPhenomC from XLALSimInspiralChooseFDWaveform. • Employed EOBNRv2 from waveforms.py in LALSuite. • Could not find original EOBNR function. LIGO Scientific Collaboration

  38. Problems Encountered • NSNS binaries had worst recovered vs expected SNR ratio. • EOBNRv2 produces EOBNRv2pseudoFourPN templates. LIGO Scientific Collaboration

  39. Signals Recovered LIGO Scientific Collaboration

  40. Omega Scan – No Glitch LIGO Scientific Collaboration

  41. Omega Scan – No Injection LIGO Scientific Collaboration

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