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# Update on machine parameters from dimuons - PowerPoint PPT Presentation

Update on machine parameters from dimuons. Josh Thompson, Aaron Roodman SLAC March 11, 2005. Toy fits. Technique: Load resolution function parameters (input) Load a prototype dataset (from real data) of phi vs docaError For each toy dataset:

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### Update on machine parameters from dimuons

Josh Thompson, Aaron Roodman

SLAC

March 11, 2005

• Technique:

• Load resolution function parameters (input)

• Load a prototype dataset (from real data) of phi vs docaError

• For each toy dataset:

• miss distance generated from resolution function PDF using prototype dataset for doca error

• doca generated from total PDF, again using prototype dataset

• z generated as a Gaussian for now

• phi and docaError generated from RooHistPdf made from prototype data

sy= 2 um

sy= 4 um

Made ntuples from May and July of 2004

Questions distribution fit

• I write the lumi distribution fit as: L(z) = exp(-2(z-z0)2/Sz2 )/Sy

• (other factors are just normalization)

• Where Sy is a function of z, z0, eyHER, eyLER, b*y

• Reasonable to assume zc = z0,HER = z0,LER?

• Reasonable to continue to fix b*y = b*yHER = b*yLER?

• How to parameterize lumi distribution fits, combined sy(z)/lumi fits

• Do we still use some “effective” emittance? Or eyHER, eyLER?

0 width MC distribution fit

• Generated a sample of MC with no beam width in y

• Fit gave: sy = 3.0139 +/- 0.32 um

• This is the same bias as always

• Could bias be due to geometric effects?

• We assume that if our tracks were perfectly reconstructed they would share a doca

• This could be violated if:

• Tracks have different momenta (small effect)

• We leave the detector (magnetic field) coordinate system where tracks go in circles in the x-y plane

• I’m trying to figure out how to actually calculate this effect

• In the mean time, I will generate some MC in an untilted frame and see if the bias is affected

MC truth from HG lumi distribution MC distribution fit

Lumi distribution using generated values

(fit to normalization)

Gaussian fit

sz=0.8

Sz,generated = 1.8

To do list distribution fit

• Run a full range of toys (0 um < sy ~< 10 um)

• Improve toy model:

• Include HG effect: generate sy(z) and run toy fits over a range of sy0, b*y

• Include HG effect in lumi distribution

• Investigate effects due to changing coordinate systems

• Leaving magnetic field frame

• If MC shows this to be the problem, I can hopefully correct for it

• txy? (too small to be the source of my bias, but it can’t hurt)

• Lumi distribution fit

• MC generation working now (?)

• also look at efficiency as a function of z

• combine w/sy(z) fit

Notes distribution fit

• Discovered something interesting about RooFit/my code:

• I load data from ntuples, apply cuts, put the data into a RooDataSet, which is saved into a file

• Then I run my resolution fit

• Later I run another resolution fit, this time loading the same data, but from the RooDataSet instead of ntuples

• The results are (very) different

• I’m guessing this is because the range of the RooRealVar docaError was not updated in the latter case (I didn’t bother to tighten it because I didn’t need to make cuts on data coming from ntuples). This variable is projected in the fit.