Hi,
here is the output...
Antonio
Heiko Lacker ha scritto:
> Hi Antionio,
>
> the errors are definitely too huge. Something must be wrong.
>
> Could you please post also the fit parameters and their errors?
>
> Heiko
>
> On Wed, 13 Sep 2006, Antonio Petrella wrote:
>
>> Ok, let's try this gaussian
>>
>> I get the following table...(errors are so huge: is it normal?)
>>
>>
>> #mx_l mx_h corr err_corr
>> 0.00 1.55 1.821 +- 0.577
>> 1.55 1.90 3.411 +- 95.487
>> 1.90 2.20 2.839 +- 96.872
>> 2.20 2.50 2.366 +- 100.470
>> 2.50 2.80 1.948 +- 106.299
>> 2.80 3.10 1.583 +- 114.362
>> 3.10 3.40 1.271 +- 124.660
>> 3.40 3.70 1.008 +- 137.194
>> 3.70 4.20 0.726 +- 157.384
>> 4.20 5.00 0.406 +- 198.663
>>
>> Antonio
>>
>> Heiko Lacker ha scritto:
>>> Hi Antonio,
>>>
>>> maybe this is not too surprising after all since the first bin
>>> contains the largest fraction of the signal.
>>>
>>> Now, that I'm thinking of it: there is a fit function which
>>> would avoid the problem of becoming negative, but which would
>>> nevertheless give probably a reasonable fit to the correction
>>> factors: a Gaussian.
>>>
>>> Cheers,
>>> Heiko
>>>
>>>
>>> On Wed, 13 Sep 2006, Antonio Petrella wrote:
>>>
>>>> Hi all,
>>>>
>>>> here are the results of the jobs with new correction factors strategy
>>>> (i.e. fit with a first order polynomial starting from the second bin):
>>>>
>>>> PBRBR= (109 +- 10 +- 4) e^-4
>>>> chi^2 of the mx fit = 25.12/7
>>>>
>>>> I also run the systematics and the value I get is
>>>> sigma=22.5%
>>>>
>>>> These are the values that I should add to the talk, but are not
>>>> encouraging...
>>>>
>>>> Antonio
>>>>
reading values from text file: corr_Wwin06_1111.txt
bin [0] = 0 1.82074 +/- 0.576767
bin [1] = 1.55 3.53191 +/- 0.756794
bin [2] = 1.9 2.72158 +/- 0.404356
bin [3] = 2.2 2.61362 +/- 0.66292
bin [4] = 2.5 2.07334 +/- 0.814513
bin [5] = 2.8 1.59886 +/- 0.942273
bin [6] = 3.1 0.907908 +/- 0.880775
bin [7] = 3.4 2.69823 +/- 3.53134
bin [8] = 3.7 0.874605 +/- 1.61436
bin [9] = 4.2 3.30556 +/- 12.5345
bin [10] = 5 0 +/- 0
<TCanvas::MakeDefCanvas>: created default TCanvas with name c1
**********
** 1 **SET ERR 1
**********
PARAMETER DEFINITIONS:
NO. NAME VALUE STEP SIZE LIMITS
1 Constant 3.21354e+00 9.64061e-01 no limits
2 Mean 2.91329e+00 8.73988e-01 no limits
3 Sigma 9.80276e-01 9.80276e-01 0.00000e+00 9.80276e+00
MINUIT WARNING IN PARAMETR
============== VARIABLE3 IS AT ITS LOWER ALLOWED LIMIT.
**********
** 2 **SET PRINT 0
**********
**********
** 3 **MIGRAD 5000 0.0001757
**********
MIGRAD MINIMIZATION HAS CONVERGED.
MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
EIGENVALUES OF SECOND-DERIVATIVE MATRIX:
-8.3360e-05 1.3927e-02 2.9862e+00
MINUIT WARNING IN HESSE
============== MATRIX FORCED POS-DEF BY ADDING 0.003070 TO DIAGONAL.
FCN=0.734914 FROM MIGRAD STATUS=CONVERGED 353 CALLS 354 TOTAL
EDM=2.55565e-10 STRATEGY= 1 ERR MATRIX NOT POS-DEF
EXT PARAMETER APPROXIMATE STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 Constant 9.88148e+00 9.75526e+00 6.57246e-04 -2.20690e-05
2 Mean -2.20089e+00 2.58442e+00 1.11402e-04 -1.28662e-04
3 Sigma 2.69156e+00 9.44711e-01 1.56112e-05 -9.02768e-04
**********
** 4 **IMPROVE
**********
EIGENVALUES OF SECOND-DERIVATIVE MATRIX:
-4.1691e-06 1.4029e-02 2.9860e+00
MINUIT WARNING IN HESSE
============== MATRIX FORCED POS-DEF BY ADDING 0.002990 TO DIAGONAL.
START ATTEMPT NO. 1 TO FIND NEW MINIMUM
**********
** 5 **HESSE
**********
FCN=0.734914 FROM HESSE STATUS=OK 16 CALLS 390 TOTAL
EDM=3.30701e-10 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER INTERNAL INTERNAL
NO. NAME VALUE ERROR STEP SIZE VALUE
1 Constant 9.88148e+00 1.31716e+02 2.62898e-05 9.88148e+00
2 Mean -2.20089e+00 4.22920e+01 4.45609e-06 -2.20089e+00
3 Sigma 2.69156e+00 6.38356e+00 6.24446e-07 -4.67726e-01
**********
** 6 **MINOS
**********
MINUIT WARNING IN MIGRAD
============== Negative diagonal element 1 in Error Matrix
MINUIT WARNING IN MIGRAD
============== Negative diagonal element 2 in Error Matrix
MINUIT WARNING IN MIGRAD
============== 1.28433 added to diagonal of error matrix
MINUIT WARNING IN MIGRAD
============== Negative diagonal element 1 in Error Matrix
MINUIT WARNING IN MIGRAD
============== Negative diagonal element 2 in Error Matrix
MINUIT WARNING IN MIGRAD
============== 3.69144e+06 added to diagonal of error matrix
EIGENVALUES OF SECOND-DERIVATIVE MATRIX:
-1.0985e+02 1.1185e+02
MINUIT WARNING IN MIGRAD
============== MATRIX FORCED POS-DEF BY ADDING 109.961067 TO DIAGONAL.
MINUIT WARNING IN MIGRAD
============== Negative diagonal element 2 in Error Matrix
MINUIT WARNING IN MIGRAD
============== 413.167 added to diagonal of error matrix
MIGRAD FAILS WITH STRATEGY=0. WILL TRY WITH STRATEGY=1.
MINUIT WARNING IN MIGRAD
============== VARIABLE3 IS AT ITS UPPER ALLOWED LIMIT.
MINUIT WARNING IN MIGRAD
============== STARTING MATRIX NOT POS-DEFINITE.
MINUIT WARNING IN MIGRAD
============== Negative diagonal element 1 in Error Matrix
MINUIT WARNING IN MIGRAD
============== Negative diagonal element 2 in Error Matrix
MINUIT WARNING IN MIGRAD
============== 5.82969e+13 added to diagonal of error matrix
MIGRAD FAILS WITH STRATEGY=0. WILL TRY WITH STRATEGY=1.
CALL LIMIT EXCEEDED IN MIGRAD.
MIGRAD FAILS WITH STRATEGY=0. WILL TRY WITH STRATEGY=1.
MINUIT WARNING IN HESSE
============== Second derivative enters zero, param 2
MINUIT WARNING IN HESSE
============== Second derivative zero for parameter2
MNHESS FAILS AND WILL RETURN DIAGONAL MATRIX.
MINUIT WARNING IN MNCROS
============== Cannot find slope of the right sign
MINUIT WARNING IN MIGRAD
============== VARIABLE3 IS AT ITS LOWER ALLOWED LIMIT.
FCN=0.734914 FROM MINOS STATUS=FAILURE 2880 CALLS 3270 TOTAL
EDM=3.30701e-10 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER PARABOLIC MINOS ERRORS
NO. NAME VALUE ERROR NEGATIVE POSITIVE
1 Constant 9.88148e+00 1.31716e+02
2 Mean -2.20089e+00 4.22920e+01
3 Sigma 2.69156e+00 6.38356e+00
FCN=0.734914 FROM MINOS STATUS=FAILURE 2880 CALLS 3270 TOTAL
EDM=3.30701e-10 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER PARABOLIC MINOS ERRORS
NO. NAME VALUE ERROR NEGATIVE POSITIVE
1 Constant 9.88148e+00 1.31716e+02
2 Mean -2.20089e+00 4.22920e+01
3 Sigma 2.69156e+00 6.38356e+00
mx = 0.775 input value 1.82074 fitted value: 5.36257
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 0.775 adding -4312.48
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 0.600625 adding 974.288
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 0.775 der. par0 1 adding -4312.48
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 0.775 der. par1 0.775 adding 1074.29
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 0.775 der. par2 0.600625 adding -243.281
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 0.600625 der. par0 1 adding 974.288
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 0.600625 der. par1 0.775 adding -243.281
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 0.600625 der. par2 0.600625 adding 55.2386
ErrBin = 106.375
mx = 1.725 input value 3.53191 fitted value: 3.41069
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 1.725 adding -9598.74
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 2.97562 adding 4826.83
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 1.725 der. par0 1 adding -9598.74
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 1.725 der. par1 1.725 adding 5322.25
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 1.725 der. par2 2.97562 adding -2682.69
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 2.97562 der. par0 1 adding 4826.83
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 2.97562 der. par1 1.725 adding -2682.69
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 2.97562 der. par2 2.97562 adding 1355.79
ErrBin = 95.4874
mx = 2.05 input value 2.72158 fitted value: 2.83914
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 2.05 adding -11407.2
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 4.2025 adding 6816.98
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 2.05 der. par0 1 adding -11407.2
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 2.05 der. par1 2.05 adding 7516.66
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 2.05 der. par2 4.2025 adding -4502.61
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 4.2025 der. par0 1 adding 6816.98
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 4.2025 der. par1 2.05 adding -4502.61
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 4.2025 der. par2 4.2025 adding 2704.28
ErrBin = 96.8724
mx = 2.35 input value 2.61362 fitted value: 2.36613
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 2.35 adding -13076.5
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 5.5225 adding 8958.18
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 2.35 der. par0 1 adding -13076.5
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 2.35 der. par1 2.35 adding 9877.63
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 2.35 der. par2 5.5225 adding -6782.76
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 5.5225 der. par0 1 adding 8958.18
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 5.5225 der. par1 2.35 adding -6782.76
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 5.5225 der. par2 5.5225 adding 4669.9
ErrBin = 100.47
mx = 2.65 input value 2.07334 fitted value: 1.94758
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 2.65 adding -14745.9
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 7.0225 adding 11391.4
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 2.65 der. par0 1 adding -14745.9
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 2.65 der. par1 2.65 adding 12560.6
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 2.65 der. par2 7.0225 adding -9726.14
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 7.0225 der. par0 1 adding 11391.4
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 7.0225 der. par1 2.65 adding -9726.14
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 7.0225 der. par2 7.0225 adding 7551.26
ErrBin = 106.299
mx = 2.95 input value 1.59886 fitted value: 1.58328
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 2.95 adding -16415.2
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 8.7025 adding 14116.5
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 2.95 der. par0 1 adding -16415.2
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 2.95 der. par1 2.95 adding 15565.4
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 2.95 der. par2 8.7025 adding -13417.4
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 8.7025 der. par0 1 adding 14116.5
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 8.7025 der. par1 2.95 adding -13417.4
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 8.7025 der. par2 8.7025 adding 11596.4
ErrBin = 114.362
mx = 3.25 input value 0.907908 fitted value: 1.27123
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 3.25 adding -18084.6
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 10.5625 adding 17133.7
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 3.25 der. par0 1 adding -18084.6
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 3.25 der. par1 3.25 adding 18892.3
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 3.25 der. par2 10.5625 adding -17941.3
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 10.5625 der. par0 1 adding 17133.7
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 10.5625 der. par1 3.25 adding -17941.3
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 10.5625 der. par2 10.5625 adding 17083.2
ErrBin = 124.66
mx = 3.55 input value 2.69823 fitted value: 1.00808
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 3.55 adding -19753.9
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 12.6025 adding 20442.8
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 3.55 der. par0 1 adding -19753.9
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 3.55 der. par1 3.55 adding 22541
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 3.55 der. par2 12.6025 adding -23382.3
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 12.6025 der. par0 1 adding 20442.8
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 12.6025 der. par1 3.55 adding -23382.3
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 12.6025 der. par2 12.6025 adding 24319.2
ErrBin = 137.194
mx = 3.95 input value 0.874605 fitted value: 0.725764
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 3.95 adding -21979.7
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 15.6025 adding 25309.2
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 3.95 der. par0 1 adding -21979.7
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 3.95 der. par1 3.95 adding 27906.9
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 3.95 der. par2 15.6025 adding -32210.2
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 15.6025 der. par0 1 adding 25309.2
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 15.6025 der. par1 3.95 adding -32210.2
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 15.6025 der. par2 15.6025 adding 37275.6
ErrBin = 157.384
mx = 4.6 input value 3.30556 fitted value: 0.405932
row 0 col 0 err.par0 131.716 err.par0 131.716 CovMat 17349 der.par0 1 der. par0 1 adding 17349
row 0 col 1 err.par0 131.716 err.par1 42.292 CovMat -5564.49 der.par0 1 der. par1 4.6 adding -25596.6
row 0 col 2 err.par0 131.716 err.par2 6.38356 CovMat 1622.12 der.par0 1 der. par2 21.16 adding 34324.1
row 1 col 0 err.par1 42.292 err.par0 131.716 CovMat -5564.49 der.par1 4.6 der. par0 1 adding -25596.6
row 1 col 1 err.par1 42.292 err.par1 42.292 CovMat 1788.62 der.par1 4.6 der. par1 4.6 adding 37847.1
row 1 col 2 err.par1 42.292 err.par2 6.38356 CovMat -522.64 der.par1 4.6 der. par2 21.16 adding -50871.7
row 2 col 0 err.par2 6.38356 err.par0 131.716 CovMat 1622.12 der.par2 21.16 der. par0 1 adding 34324.1
row 2 col 1 err.par2 6.38356 err.par1 42.292 CovMat -522.64 der.par2 21.16 der. par1 4.6 adding -50871.7
row 2 col 2 err.par2 6.38356 err.par2 6.38356 CovMat 153.121 der.par2 21.16 der. par2 21.16 adding 68559.4
ErrBin = 198.663
|
