##### Blog
Go to start of banner

# New PU acceptance systematic (For new MC signal and new binning)

I followed this recommendation

https://hypernews.cern.ch/HyperNews/CMS/get/susy/2291/1.html

to get pileup systematics.

for example I took signal point with large statistic C1N2_400_LSP25

"Define the MC efficiency in each SR i for Nvtx<20 and Nvtx>20
as e1_i and e2_i.
Plot r_i=e2_i/e1_i as a function of SR for a handful of
signal points"



"Take r = <r> \pm delta, where delta is some reasonable number
based on the plot and <r> is some number very close to 1.0."
To avoid bins with low statistic I took bins with Number of events more than 4 for both Nvtx<20 and Nvtx>20, and from them I calculated <r> and delta (as Standard deviation)

<r> = 0.998215

delta = 0.232258


A line fit to a normalized efficiency (e(N1)=1.0) is
e(Nvtx) = 1 + (r-1)*(Nvtx-N1)/(N2-N1)
You then take a TH1D of Nvtx for data and one for fastsim MC.
(These TH1D can be the same for every model and every point).
Define
default = integral of the TH1D rescaled by e(Nvtx) with r = <r>
up      = integral of the TH1D rescaled by e(Nvtx) with r = <r> + delta
down    = integral of the TH1D rescaled by e(Nvtx) with r = <r> - delta
The (normalized) acceptance with the data pileup is
default_data with (asymmetric) uncertainty (+ up_data - down_data)
Compare this with default_MC

default_data  0.999567

up_data  1.01262

down_data  0.986516

default_MC  0.999517

Table and plots for more points (will be updated with more points)

C1N2_400_LSP25C1N2_100_LSP1C1N2_200_LSP25C1N2_400_LSP200stau-stau_100_LSP1
<r>0.9982151.075511.126681.182330.994051
delta0.2322580.4112570.5054610.5621780.482308
default_data

0.999567

1.017911.033151.047040.999043

up_data

1.012621.058011.100011.128571.03639
down_data0.9865160.9777980.9662890.9655030.961694
default_MC0.9995171.019911.036631.052241.01284
The (normalized) acceptance with the data pileup is

default_data with (asymmetric) uncertainty (+ up_data - down_data)

Compare this with default_MC

Based on this comparison, assess a constant uncertainty, same over
all bins.  Make it 100% correlated across bins in combine.

Default_data with uncertainty looks in agreement with default_MC.

More plots

• No labels