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_LSP25 | C1N2_100_LSP1 | C1N2_200_LSP25 | C1N2_400_LSP200 | stau-stau_100_LSP1 | |
---|---|---|---|---|---|

<r> | 0.998215 | 1.07551 | 1.12668 | 1.18233 | 0.994051 |

delta | 0.232258 | 0.411257 | 0.505461 | 0.562178 | 0.482308 |

default_data | 0.999567 | 1.01791 | 1.03315 | 1.04704 | 0.999043 |

up_data | 1.01262 | 1.05801 | 1.10001 | 1.12857 | 1.03639 |

down_data | 0.986516 | 0.977798 | 0.966289 | 0.965503 | 0.961694 |

default_MC | 0.999517 | 1.01991 | 1.03663 | 1.05224 | 1.01284 |

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