BurstMon detection curve fit

  6/21/2004, on-line data

 master channel
 waveform file
 time stride, s
 sampling rate, Hz
 resolution, Hz
 whitening interval, s
 whitening resolution, Hz 
 percentile, %
 min cluster size
 BurstMon input buffer, s 
 dmtviewer history length 
 dump rate
 coincidence gate
 wavefilter length
 lock condition
 error limit for h50, %
 simultaneous injections
 cycles with same amplitude
 number of measurements
 H2:LSC-AS_Q
sgauss235q9.txt
60
4096
64
15
16
1
1
2
720
0
0.02
32
H2:Both_arms_locked
20
10
5
5
 h50 series

sample 1: measurements at  interval of GPS time 771914268-771914328
5 different amplitudes, N=50 injections per measurement, cluster rate - 2.9 per sec
 Comparison with Least Square Linear Fit in MS Excel, red curve on plots
amplitude efficiency x = ln(amplitude) y = ln(1/eff -1) amplitude/h50
0.00272737
0.00254915
0.00254915
0.00247727
0.002406
 0.6
0.5
0.56
0.54
0.5
 -5.904417504
-5.971995309
-5.971995309
-6.000598132
-6.029789661
 -0.405465108
0
-0.241162057
-0.16034265
0
 1.133568974
1.059495906
1.059495906
1.029620628
0.999998882

y from fit (red line on left plot) fit parameters for y(x) = a x + b
-0.374502468
-0.172637231
-0.172637231
-0.087196225
3.3393E-06		 slope (a)
intercept (b)
ln(h50) forecast (-b/a)
h50 forecast

 -2.987152931
-18.01190052
-6.029788544
0.002406003

BurstMon result:          h50 =  0.002406     err_h50  = 4.5 %
Note: error bars are for binomial distribution, p=eff, N=50,  err = sqrt(p(1-p)/N)
Fit and data points x  vs y

 Fit and data points, detection efficiency vs h/h50


sample 2:  measurements at  interval of GPS time 771914508-771914568 sec
5 different amplitudes, N=50 injections per measurement, cluster rate - 3.4 per sec 		Comparison with Least Square Linear Fit in MS Excel, red curve on plots
amplitude efficiency x = ln(amplitude) y = ln(1/eff -1) amplitude/h50
0.0026223
0.00288622
0.00281011
0.00278959
0.00273863
 0.36
0.54
0.56
0.66
0.62
 -5.943703484
-5.847807591
-5.87453165
-5.881860647
-5.900297484
 0.575364145
-0.16034265
-0.241162057
-0.663294217
-0.489548225
 0.970386429
1.068050459
1.039885829
1.032292369
1.013434537

y from fit (red line on left plot) fit parameters for y(x) = a x + b
0.245218051
-0.537040595
-0.319042454
-0.259257087
-0.10886092
 slope (a)
intercept (b)
ln(h50) forecast (-b/a)
h50 forecast

 -8.15737387
-48.23979344
-5.913642578
0.002702326

BurstMon result:          h50 =  0.00270232    err_h50  = 5.6 %
Note: error bars are for binomial distribution, p=eff, N=50,  err = sqrt(p(1-p)/N)
Fit and data points x  vs y

 Fit and data points, detection efficiency vs h/h50


sample 3:  measurements at  interval of GPS time 771915588-771915648 sec
5 different amplitudes, N=50 injections per measurement, cluster rate - 4.5 per sec 		Comparison with Least Square Linear Fit in MS Excel, red curve on plots
amplitude efficiency x = ln(amplitude) y = ln(1/eff -1) amplitude/h50
0.00260483
0.00243462
0.00280388
0.00340464
0.00287367
 0.6
0.3
0.24
0.74
0.42
 -5.950387865
-6.017964592
-5.876751107
-5.682616072
-5.85216532
 -0.405465108
0.84729786
1.15267951
-1.045968555
0.322773392
 0.89292621
0.834578843
0.961159823
1.167098156
0.98508358

y from fit (red line on left plot) fit parameters for y(x) = a x + b
0.508118721
0.811311621
0.177736603
-0.693278711
0.067428866
 slope (a)
intercept (b)
ln(h50) forecast (-b/a)
h50 forecast

 -4.486646699
-26.18916935
-5.837136532
0.002917184

BurstMon result:          h50 =  0.00291718   err_h50  = 18.3 %
Note: error bars are for binomial distribution, p=eff, N=50,  err = sqrt(p(1-p)/N)
Fit and data points x  vs y

 Fit and data points, detection efficiency vs h/h50


sample 4:  measurements at  interval of GPS time 771924408-771924468  sec
5 different amplitudes, N=50 injections per measurement, cluster rate - 3.55 per sec 		Comparison with Least Square Linear Fit in MS Excel, red curve on plots
amplitude efficiency x = ln(amplitude) y = ln(1/eff -1) amplitude/h50
0.00252779
0.00232974
0.0026831
0.00243455
0.00242968
 0.62
0.3
0.78
0.52
0.52
 -5.980409876
-6.061998606
-5.920782437
-6.017993344
-6.019995718
 -0.489548225
0.84729786
-1.265666373
-0.080042708
-0.080042708
 1.035465675
0.954337901
1.099085744
0.997271513
0.995276602

y from fit (red line on left plot) fit parameters for y(x) = a x + b
-0.495452702
0.664429745
-1.343128849
0.038841749
0.067307904		 slope (a)
intercept (b)
ln(h50) forecast (-b/a)
h50 forecast

 -14.21620914
-85.51421023
-6.015261129
0.002441211

BurstMon result:          h50 =  0.00242579    err_h50  = 3.0 %
Note: error bars are for binomial distribution, p=eff, N=50,  err = sqrt(p(1-p)/N)
Fit and data points x  vs y

 Fit and data points, detection efficiency vs h/h50