# Landau-gaussian convoluted fit looks weird

Dear experts,
When I try to make a langau fit to my histograms, for most of them, the fit works fine but for some of them, I get some strange behaviour which I cannot understand. Please can someone help?

Please have a look at the attached pdf which contains the figures and the fit output.

Kind regards,
Ruina

rootforumlangaufit.pdf (398.8 KB)

It looks like the “GSigma” can get too big. Try to define this variable as “limited” and set the upper limit to something like 10.

Thanks, I’ll work on that.

Meanwhile, do you know if there is an efficient way to give reasonable parameter values when one is fitting many distributions together? Meaning, say, I need to make these langau fits for energy distributions of ~1k electronic chips, for all of which the distribution is expected to be similar, meaning they peak at the nearly the same position etc. I guess it is possible that the start params for one distribution may not be ideal for the next…? If so, how can I solve this?

You could “estimate” them (well, maybe not all of them) from:
TH1::GetMean
TH1::GetMaximumBin + TH1::GetBinCenter
TH1::GetStdDev
TH1::Integral
TH1::GetMaximum
TH1::GetMaximumBin + TH1::GetBinContent

Yes, I had tried them in the past but somehow, they weren’t giving good results in some cases. I’ll try them again…
Thank you very much for your prompt help.

Update:
I set it to 10 and it works fine for 97 out of a 100 cases.
For 3, it is at the upper limit.

So, try to play with these 3 and e.g. set the limit to 15 (or maybe you need to set limits to another variables, too).

Yes, I already tried 15 and it works! Thanks!

However, for the the other histogram (the blue one), I had 6 of them with this warning
`Warning in <Fit>: Abnormal termination of minimization.`
and `STATUS:FAILED`
So I tried changing the start value of MPV to 35. instead of 40. and that resulted in all 6 of them getting fixed but a new one (which was fine before) getting bad . Changing start value to 34. or 36. does not help (infact they make worse the fits for others).

Failed fit examples… How do I solve this?
Example 1

``````1141 hist1 std dev 26.762
1142 hist1 mean 47.0601
1143 hist1 integral 794
1144  FCN=243.635 FROM MIGRAD    STATUS=FAILED        566 CALLS         567 TOTAL
1145                      EDM=0.523113    STRATEGY= 1  ERROR MATRIX UNCERTAINTY  10.5 per cent
1146   EXT PARAMETER                APPROXIMATE        STEP         FIRST
1147   NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE
1148    1  Width        1.27874e-02   2.39655e-04   0.00000e+00  -1.51826e+02
1149    2  MP           1.30452e+01   6.92787e-04   0.00000e+00  -1.08202e+06
1150    3  Area         1.33779e+04   2.59424e+03   0.00000e+00   2.15953e+00
1151    4  GSigma       9.99316e+00   3.68144e-04  -0.00000e+00   1.71252e+02
``````

Example 2

``````25603 hist1 std dev 24.9358
25604 hist1 mean 51.7143
25605 hist1 integral 14599
25606  FCN=293.288 FROM MIGRAD    STATUS=FAILED        246 CALLS         247 TOTAL
25607                      EDM=10738.8    STRATEGY= 1  ERROR MATRIX UNCERTAINTY 100.0 per cent
25608   EXT PARAMETER                APPROXIMATE        STEP         FIRST
25609   NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE
25610    1  Width        3.16788e+00   6.53764e+00   0.00000e+00  -2.99869e+02
25611    2  MP           3.87556e+01   6.08231e+04  -0.00000e+00   2.87698e+04
25612    3  Area         1.48061e+04   5.61181e+05  -0.00000e+00   1.87737e+02
25613    4  GSigma       9.40323e+00   7.00875e+00  -0.00000e+00   1.30095e+02
``````

The errors are quite large and I don’t know why the step size is zero for all the parameters!

Hi,

Finding good initial values is the most tricky part in minimization / fitting: it can determine success or failure and can change the result. I don’t think we have any generic recommendations here; this depends too much on the actual problem / distribution. So indeed your best option is likely to take these distributions and use the fit panel (right click the histogram in a pad) to come up with good initial parameter values for a landau distribution.

Axel.

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