[Rivet] Question on normalisation of histograms containing weighted events

[Holger Schulz]

On 17/07/12 19:47, Andy Buckley wrote:
> A very quick reply...
>
> This plot is marked as thrust, but the shape suggests to me that it's
> actually 1-T, i.e. the perfect back-to-back dijet config is at 0 rather
> than 1: is that correct?
No, it's (transverse) thrust, calculated for all charged particles
in Z->ee events (minus the leptons from the Z-decay).
>
> If so, then it looks like your glued distribution is more spherical than
> the true inclusive distribution, which would imply that too much weight
> is going on high #MPI, or too little on #low MPI. So, some questions:
>
> * have you included a #MPI = 0 "bin"?
Yes.
> * I think it's worth double checking that everything is consistent in
> the normalisation, since Rivet now includes the overflow and underflow
> bins in calls to normalize()
Will do.

Any feeling if the normalistion kind of destroys information
from the weighted events? The #MPI histo is also filled using the
weights, so weight information should be recoverable, right?
> * double-check that there's no bin-width silliness going on!
>
> Finally, just a comment that I don't think #MPI is not really a physical
> variable, but more an artefact of the modelling with multiple
> perturbative scatters. Is the analysis attempting to *measure*<#MPI>,
> or is that just an intermediate thing that you're e.g. reweighting?
The analysis is going to measure event shapes, as a bonus we
would like to calculate (the model-dependent) sigma_eff
which is possible by means of a template fit and the knowledge
of the model-dependent 2->2 MPI cross-section up to our process-scale,
m(Z).

Holger
>
> Andy
>
>
> On 17/07/12 18:33, Holger Schulz wrote:
>> Hi,
>>
>> I am working on a template fit for some event shape
>> distributions. In each event, I ask for the number of MPI scatters
>> and thus get the distributions differential in #MPI.
>> Those distributions are not normalised in finalize().
>>
>> I also obtain a histogram with the occurences of
>> the number of MPI-scatters, which I normalise to one.
>>
>> Further I fill a histogram that contains the inclusive
>> distribution, which I normalise to 1.
>>
>> When I try to "glue" the differential distributions
>> together, using the relative #MPI contributions
>> and afterwards scale the glued distribution to 1,
>> I do not get the same shape as is present in the
>> inclusive histo (see attachment).
>>
>> The event generator I use is Sherpa, generating
>> weighted events and this is rivet still using the AIDA
>> histogramming.
>>
>> Does anyone have a feeling if the weighted events
>> and my normalisations do not get along well?
>>
>> Cheers,
>> Holger
>>
>>
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>