The Block-Normal Event Trigger Generator John W. C. M c Nabb, for - PowerPoint PPT Presentation

LIGO The Block-Normal Event Trigger Generator John W. C. M c ¯ Nabb, for The Penn State University Relativity Group Mike Ashley, Lee Samuel Finn, John M c ¯ Nabb, Eric Rotthoff, Amber Stuver, Tiffany Summerscales, Matt Tibbits, Keith Thorne, Tamara Valinoto, Kristina Zaleski mcnabb@gravity.psu.edu Penn State Center for Gravitational Wave Physics LIGO Scientific Collaboration McNabb GWDAW-8: Milwaukee,Wisconsin 1/15

Outline LIGO Block-Normal Overview From Time-Series to Triggers Change Points Blocks and Events Clusters to Triggers Tuning Block-Normal Thresholds Sensitivity to Block-Normal parameters Reconstruction of Simulations Conclusions McNabb GWDAW-8: Milwaukee,Wisconsin 2/15

The Problem LIGO Finding unmodeled bursts in time-series. “Unmodeled” means one must look for general features of bursts. Allows for many different methods. A solution: Block-Normal generates triggers for more computationally expensive multi-interferometer analysis McNabb GWDAW-8: Milwaukee,Wisconsin 3/15

Block-Normal Overview LIGO Baseband data into frequency bands Break data into blocks : Character of data changes between blocks . Character of data within a block roughly constant. Cut on “unusual” blocks : events Collect adjacent events into a cluster Look for coincident clusters to generate triggers McNabb GWDAW-8: Milwaukee,Wisconsin 4/15

Block-Normal is a spectral analysis LIGO Data conditioning steps: Frequency(Hz) basebanding, line removal, whitening 128-192 Analysis carried out on separate 192-320 bands 384-512 Coincidence insists on events with 512-640 same spectral character 704-1024 Choice of bands: 1065-1365 Avoid violin modes which are too 1408-1708 non-stationary to track 1758-2048 note 900-930 Hz band for 900-930 comparison with bar results McNabb GWDAW-8: Milwaukee,Wisconsin 5/15

☎ ✆ ☛ ☞ ✆ ☛ ☎ ✆ ✄ ☎ ✆ ✄ ✄ Change Points LIGO Characterize data using parameters of normal distributions: mean and variance. Find change points the probability of data to either side being �✂✁ drawn from one distribution. the probability of the data being drawn from �✂☎ two different distributions. ✝✟✞ ✝✡✠ If change point found: ( ). iterate over the two newly formed blocks. Second pass examining consecutive blocks: in the limited interval is change point still significant? ( ) ✆✍✌ McNabb GWDAW-8: Milwaukee,Wisconsin 6/15

✁ � ✂ ☎ ✠ ☛ ☎ ✁ � ✂ ☎ ✁ ✂ ✡ ✟ ☎ ☛ � ✝ ☎ ✁ ✁ ☎ � ✂ � ✝ � Blocks LIGO blocks characterized by: start and end times mean( ) variance( ) events defined by: comparing block ’s character to that of long (>50s) epoch ( ) ✂☎✄ OR ✆✞✝ and are called event thresholds ✆✞✝ McNabb GWDAW-8: Milwaukee,Wisconsin 7/15

Triggers LIGO time L1 Cluster adjacent events H1 H2 frequency band into a single cluster . L1 H1 H2 Calculate calibrated L1 H1 H2 energy of cluster . Coincidences: Do coincidence between detectors based on: frequency band time of “loudest” block within cluster. consistency of calibrated energy. consistency of duration. Clusters that pass get marked as triggers . McNabb GWDAW-8: Milwaukee,Wisconsin 8/15

☞ ✆ ✝ � Intermission LIGO Block-Normal is a time-domain search for bursts that Breaks data into roughly stationary normal blocks . Identifies unusual blocks as events Triggers on coincident clusters of events in multiple interferometers. Characterizes triggers by Frequency, Energy, Duration, and Peak-time in each IFO. Parameters of the search are the frequency bands, the change point thresholds ( , ) and the event ✆✍✌ thresholds ( , ) ✆✞✝ McNabb GWDAW-8: Milwaukee,Wisconsin 9/15

Change Point Thresholds LIGO Change-Point false rate as function of acceptance threshold for different data segment lengths, variances McNabb GWDAW-8: Milwaukee,Wisconsin 10/15

✔ ✟ ✠ ✑ ✄ ✒ ✓ ✖ ✕ ✄ ✖✗ ✗ ✟ ✧ ✘ � ✟ ✡ ✄ ✙ ✟ ☎ ✚ ✖ ✛ ✁ ✜✢ � ✁ ✣ ✢ ✡ ✂ ✁ ★ ★ ✛ � ✡ ✡ ✄ � ✂ ✄☎ ✆ ✠ ✝ ✝ ✞ ✡ ✠ ✞ ☎ ☛ ✞ ☞✌ ✍ ✟ ✎ ✏ ✑ ✡ ✟ ✁ ✤ Sensitivity/Effi ciency LIGO Tune for best in-band signal sensitivity Model signal: ✝ ✠✟ ✟ ✂✁ band 1: Hz , ms Four-parameter efficiency model ✤✦✥ Fit accommodates non-zero background McNabb GWDAW-8: Milwaukee,Wisconsin 11/15

Sensitivity/Effi ciency LIGO 1 H1 H2 L1 0.8 0.6 ε 0.4 0.2 0 E 1/2 = ( ∫ h 2 dt) 1/2 McNabb GWDAW-8: Milwaukee,Wisconsin 12/15

Reconstruction of Simulations: LIGO 10 Lock 61 band 1 IFO H1 CG 576 Hz τ =100 ms Event time precision (samples) 8 ρ A ρ R [9 8] Timing: µ T ν T [2 2] Central 50 quantile 6 Event time: time at 4 reconstructed signal peak 2 Precision: 0 E 1/2 h (h sec 1/2 ) Better than 8 samples for 50 50% of detected signals Lock 61 band 1 IFO H1 CG 576 Hz τ =100 ms Event time precision (samples) [(25,75) quantile range] 40 ρ A ρ R [9 8] µ T ν T [2 2] Central 90 quantile Better than 40 samples for 30 90% of detected signals 20 [(5,95) quantile range] 10 0 1/2 (h sec 1/2 ) E h McNabb GWDAW-8: Milwaukee,Wisconsin 13/15

Reconstruction of Simulations: LIGO “Energy” Squared strain in event E r (h 2 s) Reconstruction is very precise Reconstruction is accurate at level of the calibration systematics E i (h 2 s) McNabb GWDAW-8: Milwaukee,Wisconsin 14/15

� ✁ Conclusions LIGO Block-Normal parameters: Change-Point thresholds control sensitivity to changes in mean, variance; specified by desired change-point false rate Event thresholds control sensitivity to differences between block, background character; specified relative to background mean and variance. Sensitivity: Comparable to existing burst search methods Reconstruction: Energy accurate at level of calibration systematics precise to greater than calibration systematics Time Resolution: samples McNabb GWDAW-8: Milwaukee,Wisconsin 15/15