How to consistently vary SCALUP at NLO

Asked by J. Heisig on 2019-07-03

Hi,

We are studying a process which exhibits a large dependence on the SCALUP variable chosen, see earlier question:
https://answers.launchpad.net/mg5amcnlo/+question/679144

Now we'd like to vary SCALUP (the starting scale of the shower) in the NLO interface. How can we do this consistently without messing up with counter terms or so. I guess one cannot just rescale it in the event files but has to do it in MadGraph. Could someone please help?

Thanks!
Best,
Jan

Question information

Language:
English Edit question
Status:
Answered
For:
MadGraph5_aMC@NLO Edit question
Assignee:
Paolo Torrielli Edit question
Last query:
2019-07-17
Last reply:
2019-07-20

Dear Jan,
in the run_card.dat file of the NLO interface you can find a parameter

1.0 = shower_scale_factor ! multiply default shower starting
                                  ! scale by this factor

Varying this parameter will result in a variation of the range in which
SCALUP values are picked (not a in rescaling of the scale itself).
I'd say that 0.5 and 2.0 could be sensible values for variation.

Cheers,
Paolo

J. Heisig (heisig) said : #2

Hi Paolo,

Thanks for the answer. However, varying shower_scale_factor does not effect our results (within statistical errors) at all.

For the considered model we've seen quite some dependence on the shower scale at LO (varying SCALUP in the lhe file 'by hand'). In the NLO interface, I would guess that if SCALUP values are picked from a much larger range it should effect the result in some way. Maybe you could explain in a bit more detail what shower_scale_factor actually does and how we could investigate the dependence on the shower starting scale with it.

Thanks and best regards
Jan

Dear Jan,
you could plot the SCALUP distribution, to check how it is different
from the one of the original event file. The shower_scale_factor
parameter just makes this distribution more or less broad.
In general, one does not expect a large dependence upon this SCALUP at
NLO+PS, barring the case of observables that are particularly
sensitive to shower effects, such as the transverse momentum of the
Born-level system, but still identical physical distributions are
suspect.
You could also check whether two event files generated with the very
same random seed and different shower_scale_factor differ just by the
SCALUP values, as expected.
Cheers.
Paolo

2019-07-17 16:43 GMT+02:00, J. Heisig <email address hidden>:
> Question #681777 on MadGraph5_aMC@NLO changed:
> https://answers.launchpad.net/mg5amcnlo/+question/681777
>
> Status: Answered => Open
>
> J. Heisig is still having a problem:
> Hi Paolo,
>
> Thanks for the answer. However, varying shower_scale_factor does not
> effect our results (within statistical errors) at all.
>
> For the considered model we've seen quite some dependence on the shower
> scale at LO (varying SCALUP in the lhe file 'by hand'). In the NLO
> interface, I would guess that if SCALUP values are picked from a much
> larger range it should effect the result in some way. Maybe you could
> explain in a bit more detail what shower_scale_factor actually does and
> how we could investigate the dependence on the shower starting scale
> with it.
>
> Thanks and best regards
> Jan
>
> --
> You received this question notification because you are subscribed to
> the question.
>

Can you help with this problem?

Provide an answer of your own, or ask J. Heisig for more information if necessary.

To post a message you must log in.