MadGraph5_aMC@NLO

How MadGraph specific is the "systematics" code?

Asked by Kenneth Long

Dear experts,

In order to consider many PDF sets, we are often generating a huge number of sets and variations, stored as weights for an experimental analysis. To avoid having all of these stored to disk, it could be useful to adopt an on-the-fly approach to recompute weights, as done in your systematics program.

Within CMS, we convert the LHE file to a custom format, but I would imagine that if we store the same info as MG stores per-event with store_rwgt_info = True and then write a wrapper for the "Event" class from lhe_parser.py to read our format, such an on-the-fly production of the weights at analysis time could work. My main question is, would this be extendable to other generators? That is, are there fundamental assumptions (e.g. in POWHEG vs. MC@NLO) that would prevent the algorithm from working, or is it just a matter of trying to extract the relevant info from the generator and store it in the same format in the LHE file?

Thanks,

Kenneth

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Olivier Mattelaer (olivier-mattelaer) said : 		#1

Dear Kenneth,

I do not think that it would be impossible to setup a common format for both Powheg/MC@NLO.
I guess that this can be a good project for LesHouches.
So yes like this I do not see any fondamental issue (now the current format is MG5aMC focus obviously)

Cheers,

Olivier
> On 24 Oct 2019, at 15:08, Kenneth Long <email address hidden> wrote:
>
> New question #685376 on MadGraph5_aMC@NLO:
> https://answers.launchpad.net/mg5amcnlo/+question/685376
>
> Dear experts,
>
> In order to consider many PDF sets, we are often generating a huge number of sets and variations, stored as weights for an experimental analysis. To avoid having all of these stored to disk, it could be useful to adopt an on-the-fly approach to recompute weights, as done in your systematics program.
>
> Within CMS, we convert the LHE file to a custom format, but I would imagine that if we store the same info as MG stores per-event with store_rwgt_info = True and then write a wrapper for the "Event" class from lhe_parser.py to read our format, such an on-the-fly production of the weights at analysis time could work. My main question is, would this be extendable to other generators? That is, are there fundamental assumptions (e.g. in POWHEG vs. MC@NLO) that would prevent the algorithm from working, or is it just a matter of trying to extract the relevant info from the generator and store it in the same format in the LHE file?
>
> Thanks,
>
> Kenneth
>
> --
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