MadGraph5_aMC@NLO

MG5 v3.3.1, INFO: fail to reach target number of events

Asked by Shudong Wang

Hi,
I am trying to generate some samples using MG5 v3.3.1 and used the following commands in MG5:
---------------------
define down_type_q = d s b d~ s~ b~
define up_type_q = u c t u~ c~ t~
define charged_lepton = e- e+ mu- mu+ ta- ta+
define neutrino = ve ve~ vm vm~ vt vt~

import model sm-ckm

generate e- e+ > down_type_q down_type_q charged_lepton neutrino charged_lepton neutrino QED<=99 QCD<=99

output emep_ttbar_dtdtlvlv

launch emep_ttbar_dtdtlvlv/
  shower=Pythia8
  0
  set ebeam1 180
  set ebeam2 180
  set nevents 20000
  0
exit
---------------------
But the code failed to reach the target number of events :

......
INFO: Combining runs
sum of cpu time of last step: 3 days,23h46m34s
INFO: finish refine
INFO: Combining Events
INFO: fail to reach target 20000
=== Results Summary for run: run_01 tag: tag_1 ===

      Cross-section : 0.023 +- 0.0008455 pb
      Nb of events: 64

store_events
INFO: Storing parton level results
INFO: End Parton
......

I have no idea what's wrong, could you please help me with that?

Best,
Shudong

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Solved by:
Olivier Mattelaer
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Best Olivier Mattelaer (olivier-mattelaer) said : 		#1

Hi,

Do you really need CKM for this process?
Such computation is already quite complex like that and if you do not have a good reason to include the full ckm matrix, I would advise to use a simpler CKM matrix for the production, you will avoid a lot of numerical issue and speed up the code quite a lot.
Then you may want to use the hard_survey parameter to increase the number of points in the integration to increase the number of points generated. And check which of the sde_strategy is used for this process. In this case I would advise the sde_strategy=2 but this is likely the default. In any case, i would advise to test both (1 or 2) and check which one works best.

Cheers,

Olivier

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Shudong Wang (sfsvds) said : 		#2

Thanks Olivier Mattelaer, that solved my question.