# How to consistently vary SCALUP at NLO

Hi,

We are studying a process which exhibits a large dependence on the SCALUP variable chosen, see earlier question:

https:/

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

- 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

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:/

>

> 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

>

> --

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> the question.

>

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