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Search for Spinning Black Hole Binaries in Advanced LIGO: Parameter tuning of HACR

Search for Spinning Black Hole Binaries in Advanced LIGO: Parameter tuning of HACR. Speaker: Gareth Jones Cardiff University LSC Meeting March 2005 ASIS session DCC#: G050201-00-Z. Overview. Goal: Detection of SBBH’s in Time Frequency representation of Advanced LIGO data What is HACR?

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Search for Spinning Black Hole Binaries in Advanced LIGO: Parameter tuning of HACR

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  1. Search for Spinning Black Hole Binaries in Advanced LIGO: Parameter tuning of HACR Speaker: Gareth Jones Cardiff University LSC Meeting March 2005 ASIS session DCC#: G050201-00-Z Spinning Black Hole Binaries

  2. Overview Goal: Detection of SBBH’s in Time Frequency representation of Advanced LIGO data • What is HACR? • Spinning Black Hole Binaries • Tuning HACR in 2 steps • Results • Conclusions and Future Work Spinning Black Hole Binaries

  3. What is HACR? • Hierarchical Algorithm for Clusters and Ridges written by R. Balasubramanian • Time-frequency search • Short bursts of excess power • More robust than matched-filtering • Implemented in GEO++ as HACRMon Spinning Black Hole Binaries

  4. How HACR works 1. Identify pixels with P > ηupper Frequency Time Spinning Black Hole Binaries

  5. How HACR works 1. Identify pixels with P > ηupper “Black pixels” Frequency Time Spinning Black Hole Binaries

  6. How HACR works 2. Identify neighbouring pixels with P > ηlower “Black pixels” Frequency Time Spinning Black Hole Binaries

  7. How HACR works 2. Identify neighbouring pixels with P > ηlower “Cluster” Frequency Time Spinning Black Hole Binaries

  8. How HACR works 3. Identify clusters with Npixels > Nthreshold “Cluster” Frequency Time Spinning Black Hole Binaries

  9. How HACR works 3. Identify clusters with Npixels > Nthreshold “Event” Frequency Time Spinning Black Hole Binaries

  10. Spinning Black Hole Binaries • Require 17 physical parameters to accurately describe waveform • Spin-induced precession of orbital plane causes modulation of amplitude and phase • Amplitude modulation causes of GW “archipelago” of clusters in TF map Spinning Black Hole Binaries

  11. Spinning Black Hole Binaries Amplitude Amplitude Time Time Frequency Frequency Time Time Spinning Black Hole Binaries

  12. Tuning HACR For a given false alarm rate maximise probability of detection • Identify combinations of threshold parameters (ηupper,Nthreshold ) that produce a constant false alarm rate… • … then for each combination of parameters measure probability of detection Spinning Black Hole Binaries

  13. False Alarm Analysis • 100,000s of simulated Adv. LIGO data • Analyse with low values of ηupper and Nthreshold • Store maximum-power and number of pixels for each found “event” • For range of ηupper choose values of Nthreshold that give specific False Alarm rates Spinning Black Hole Binaries

  14. False Alarm Analysis Contours of constant false alarm rate 1 False alarm per… Nthreshold …hour (27.8 events) …1000s (100 events) …10 mins (166.7 events ) …100s (1000 events) …min (1666.7 events) …10s (10,000 events) ηupper Spinning Black Hole Binaries

  15. Injection of SBBH waveforms • Physical waveforms • (1-49), (10-40) and (25-25)Msolar • 1000 injections each with SNR = 8,12,16 • Measure number of injections recovered for each combination of (ηupper,Nthreshold ) Spinning Black Hole Binaries

  16. Results SNR = 16 # of recovered injections SNR = 12 SNR = 8 Expected # of FA = ~54 ηupper Spinning Black Hole Binaries

  17. Results Small spin modulation Large spin modulation (1-49)M (1-49)M (10-40)M (10-40)M (25-25)M (25-25)M Spinning Black Hole Binaries

  18. Results Small spin modulation Large spin modulation (1-49)M (1-49)M (10-40)M (10-40)M (25-25)M (25-25)M ηupper = 23, Nthreshold = 50 Spinning Black Hole Binaries

  19. Conclusion and Future Work • More injections and at various distances • Power law pattern matching for potential event clusters • Tune HACR for EMRI signals in LISA (with Jonathan Gair) Spinning Black Hole Binaries

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