EDM in RooFit

Dear experts,

I have a general question about the EDM value of the roofit.
Is it necessary to be less than <1e-6 to indicate a good fit? I know it is used to control the stop of the minimization. By default setting, I usually get an EDM value like 1e-4 ~1e-5, do I need to change the tolerance?

e.g I am performing a UML fit, the output is like following and the fit likes fine.

the output from the fitting

COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=-2.48097e+07 FROM HESSE STATUS=OK 40 CALLS 324 TOTAL
EDM=0.000311126 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER INTERNAL INTERNAL
NO. NAME VALUE ERROR STEP SIZE VALUE
1 lambda_Bu -3.21855e-03 3.08810e-05 7.78062e-07 -6.43711e-04
2 mean_Bu 5.27866e+03 3.47285e-02 2.52633e-04 1.55008e-01
3 nbkg 2.85245e+05 8.92294e+02 1.25842e-04 -9.24946e-01
4 nsig 2.45351e+06 1.72176e+03 2.30789e-04 8.21959e-01
5 sigmaL_Bu 1.69468e+01 2.71322e-02 2.09937e-04 -3.10274e-01
6 sigmaR_Bu 1.82945e+01 2.55485e-02 1.88080e-04 -1.71392e-01
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 6 ERR DEF=0.5
9.536e-10 3.269e-07 -1.098e-03 1.099e-03 2.833e-07 -2.605e-07
3.269e-07 1.206e-03 -3.079e+00 3.078e+00 7.430e-04 -7.202e-04
-1.098e-03 -3.079e+00 7.962e+05 -5.109e+05 -7.400e+00 -1.943e+00
1.099e-03 3.078e+00 -5.109e+05 2.964e+06 7.400e+00 1.943e+00
2.833e-07 7.430e-04 -7.400e+00 7.400e+00 7.362e-04 -3.593e-04
-2.605e-07 -7.202e-04 -1.943e+00 1.943e+00 -3.593e-04 6.527e-04
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2 3 4 5 6
1 0.40200 1.000 0.305 -0.040 0.021 0.338 -0.330
2 0.92004 0.305 1.000 -0.099 0.051 0.788 -0.812
3 0.48637 -0.040 -0.099 1.000 -0.333 -0.306 -0.085
4 0.34337 0.021 0.051 -0.333 1.000 0.158 0.044
5 0.84451 0.338 0.788 -0.306 0.158 1.000 -0.518
6 0.84970 -0.330 -0.812 -0.085 0.044 -0.518 1.000

Best,
Quan

Hello,

@moneta or @jonas, please :slight_smile:

Hi,
I think an EDM of ~1E-4, 1.E-5 is good enough, if it is computed correctly.
It is often important to compute the Hessian, otherwise the EDM is computed with an approximate Hessian and it can be actually larger than what has been computed.
So, if you want to save some time in the minimization, you can reduce slightly the tolerance

Lorenzo

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