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DMT Monitor Verification with Simulated Data

John Zweizig LIGO/Caltech. DMT Monitor Verification with Simulated Data. Verification: Current Practices. Test trigger generation using IFO data Noise is correct Difficult to verify calculations Manual ID of “non-gaussian” events Good feel for data, but Tedious – error prone

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DMT Monitor Verification with Simulated Data

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  1. John Zweizig LIGO/Caltech DMT Monitor Verification with Simulated Data

  2. Verification: Current Practices • Test trigger generation using IFO data • Noise is correct • Difficult to verify calculations • Manual ID of “non-gaussian” events • Good feel for data, but • Tedious – error prone • Limited samples – not thorough • Difficult to quantify efficiencies, resolutions  Important but insufficient verification

  3. Verification with Simulated Data • Test with real IFO or generated noise • Intermediate result distributions know for generated noise • Can test effect of specific features (e.g. lines) • Inject known trigger sources • Measure efficiency vs. F, Amplitude, width, etc. • Measure efficiency for different waveforms (sine gaussian , gaussian noise burst, damped sine, etc.) • Measure resolution of inferred parameters (t, F, etc.).

  4. DMT Simulation Package • DMTGen Features: • Combines discrete signals with continuous background noise (generated or from frames) • Write output data to frames (looks like raw data) • Stand-alone program (no coding, compilation or re-scripting) • Simple control syntax • Event parameters and signals recorded in output frames • Arbitrary filtration/delay • Fast:

  5. # These parameters define the times for the generated data. # Parameter StartGPS 730000000 Parameter EndGPS 730000480 # # Define a source of background white gaussian noise # Source GS WhiteNoise(A=2.0) # # Define a source of periodic noise bursts. These will be produced # with varying amplitudes (dN/dA ~ A^{-2}) and a width (sigma) of # 10ms. The time of the burst will be random with an average rate # of 0.2 Hz. Note the -simevent flags causes DMTGen to write a # description of each generated even to the output frame # Source GB GaussBurst(A=power(-2,2),Sigma=0.010) -rate 0.2 -simevent # # Write a channel called "L1:LSC-AS_Q" consisting of the sum of the # Background noise and data sources. # Channel L1:LSC-AS_Q GS GB # DMTGen Control Syntax • Parameter definitions • Set run parameter values • Filter Statements • Define filters, delays, etc. • Source definitions • Define continuous or discrete data sources • Specify timing, enable saving to frames • Channel definitions • Channels are a sum of sources, written to output frame

  6. Discrete Signal Sources • Analytic functions • SinGauss(A, F, Q, Phi, Width) • DampedSine(A, F, Q, Phi, Width) • GaussBurst(A, Sigma, Width) • Single injections, constant or random time separation. • Fixed, random function parameters, optionally recorded in frame. • Event data recorded in FrSimData structures (optional) • Arbitrary filtration or delay applied individually.

  7. Waveform Parameter Generation • Parameter distributions • Constant or string • flat(min, max): dN/dx ~ k • step(x0, xmax, ): • x = x0, x0+, x0+2, …, xmax • xstep(x0, xmax, ): • x = x0, x0, x02, …, xmax • gauss(, <x>): • dN/dx ~ exp(–(x-<x>)2/22) • power(b, min, max): • dN/dx ~ xb • exp(b, min, max): dN/dx ~ e-bx

  8. Background Noise Generation • Continuous waveform sources • WhiteNoise(A) • Sine(A, F, Phi) • FrameData(Channel, Files)

  9. MatchTrig - Trigger Checking • Assign each generated event to the nearest trigger. • Triggers are read from Monitor xml output file or Data Base. • Event parameters are recorded by DMTGen in frames. • Plot trigger efficiency versus: • Amplitude, frequency, other generation parameters • Time since previous event • Plot reconstructed parameter resolution • Time, amplitude, frequency versus generated parameters

  10. MatchTrig – PSLmon results

  11. Writing Verifiable Trigger Code • Start with finest ingredients (verified components) • Look at every cut quantity • Histograms or spectra of intermediate results • Trip counters after each cut • Measure trigger efficiency versus generated signal parameters • Measure t, F, A resolution • Verify significance calculations: • Trigger rate for gaussian noise should be SR/erf(thresh)

  12. Summary • We neeed to improve on current techniques of DMT trigger generator verification. • DMTgen provides easily understood data for use in verifying DMT code. • MatchTrig compares trigger results to generated event parameters – gives efficiencies and resolution. • Verification of histograms of intermediate results is facilitated by using known input distributions.

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