How should I be generating the LO and NLO parts of pp> H + jets for merging with Pythia?

Asked by Joshua Lin on 2018-06-12

My goal is to generate samples of p p > H + jets (in the regime of high Higgs transverse momentum), and to use merging schemes such as UNLOPS in Pythia on the data. The pythia page suggests that I generate "a) leading-order inputs generated with the leading-order mode of aMC@NLO, using the UNLOPS prescription, and b) next-to-leading-order inputs generated with the NLO mode of aMC@NLO, using the UNLOPS prescription."

I have been trying to get a working setup for a while but have been unable. My plan was to try generating p p > H j at NLO, and p p > H j j @LO, then use UNLOPS to merge the two. But if I try the command:
p p > H j [QCD]
this doesn't actually generate at NLO, because there are no tree level diagrams in the first place, so when I subsequently launch the process it isn't using aMC@NLO, but rather just madgraph (what I mean by that is if for example I look at the run card, it is labelled as run_card.dat Madevent and not run_card.dat aMC@NLO, so for the ickkw option that controls the merging, there is no option for UNLOPS)

Is it sensible to just do a LO scheme like MLM in this case? I seem to be getting the wrong cross-sections for now when I try LO matching but that might just be my code not up to scratch

Thanks for any help

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MadGraph5_aMC@NLO Edit question
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Last query:
2018-06-12
Last reply:
2018-06-13

Hi,

Is it sensible to just do a LO scheme like MLM in this case?

Well everything depends of the precision that you want to achieve, the cpu time that you have available,...
and in fine to the observables that you are looking at.

The most accurate method for such generation is using a hgg effective vertex at NLO accuracy.
But using the re-weigthing capabilities of MG5aMC to reintroduce the finite top mass effect of the shrunked loop. This has been tested/validated with FxFx by Eleni Vryonidou and also by CMS generator group.
To reach high accuracy in the tail, they also use the bias method.
I would suggest to contact Eleni for more information if you want to go that way.

I'm not sure that such re-weighting is fully compatible with UNLOPS so if you face any problem, please tell us.

Cheers,

Olivier

On 13 Jun 2018, at 01:17, Joshua Lin <<email address hidden><mailto:<email address hidden>>> wrote:

New question #670175 on MadGraph5_aMC@NLO:
https://answers.launchpad.net/mg5amcnlo/+question/670175

My goal is to generate samples of p p > H + jets (in the regime of high Higgs transverse momentum), and to use merging schemes such as UNLOPS in Pythia on the data. The pythia page suggests that I generate "a) leading-order inputs generated with the leading-order mode of aMC@NLO, using the UNLOPS prescription, and b) next-to-leading-order inputs generated with the NLO mode of aMC@NLO, using the UNLOPS prescription."

I have been trying to get a working setup for a while but have been unable. My plan was to try generating p p > H j at NLO, and p p > H j j @LO, then use UNLOPS to merge the two. But if I try the command:
p p > H j [QCD]
this doesn't actually generate at NLO, because there are no tree level diagrams in the first place, so when I subsequently launch the process it isn't using aMC@NLO, but rather just madgraph (what I mean by that is if for example I look at the run card, it is labelled as run_card.dat Madevent and not run_card.dat aMC@NLO, so for the ickkw option that controls the merging, there is no option for UNLOPS)

Is it sensible to just do a LO scheme like MLM in this case? I seem to be getting the wrong cross-sections for now when I try LO but that might just be my code not up to scratch

Thanks for any help

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