Search for Stable Massive Particles
5.8 Systematic Uncertainties
5.8.4 Initial-state radiation
TheEmissT trigger relies primarily on an energy imbalance which orig- inates from ISR. With the effects of underlying event and FSR negli- gible, the ISR modelling can have a strong impact on the efficiency of the EmissT trigger and thus the signal yield. It is therefore neces- sary to estimate a systematic uncertainty on the ISR description in simulation.
In chapter 5.1it was described howR-hadron samples generated in Py t h i aare reweighted to match the ISR spectrum in Ma dGr a p h. A systematic uncertainty on this procedure is obtained by comparing the signal yield after trigger selection with the reweighting applied and turned off. The uncertainty is taken as half the difference and is estimated individually for every mass point in the MS-agnostic and full-detector selection. The relative systematic uncertainties are given in figure5.33.
Figure5.33: Relative systematic uncer- tainty on the modelling of ISR for allR- hadron mass points. The left plot shows the uncertainty in the MS-agnostic trig- ger selection, the right plot for the full- detector selection where uncertainties tend to be slightly lower due to the additional single-muon trigger. A flat systematic uncertainty visualised by the red dashed line is assigned.
500 1000 1500 2000 2500 3000 mass [GeV] 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 ISR uncertainty gluinos sbottoms stops = 13 TeV, MC simulation s MS agnostic 500 1000 1500 2000 2500 3000 mass [GeV] 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 ISR uncertainty gluinos sbottoms stops = 13 TeV, MC simulation s Full detector
A mass dependency of the uncertainty is clearly visible24 . A
24
The initial rise is due to the larger amount of available energy, while the subsequent falloff is caused by the change in dominant production chan- nel from gluon–gluon fusion to quark– antiquark production and thus reduced ISR emission
conservative approach is chosen which assigns a systematic uncer- tainty of 15% to all masses and particle types. Since large contribu- tions from the gluon–gluon fusion production channel are absent for bottom and top squarks, the probability for additional radiation in
Ma dGr a p h is smaller and thus the uncertainty estimated from the difference results in smaller uncertainties for squarks.
Stau and chargino sample are already produced in Ma dGr a p h and no reweighting is needed. The uncertainty on the ISR mod- elling can be estimated by varying different generator parameters independently of each other. The first variation scales the chosen renormalisation/factorisation parameter downwards by a factor of 0.5(q c dw) and upwards by2.0(q c u p). The uncertainty due to the CKKW-L25
merging scale is similarly evaluated by applying varia- 25
An algorithm which addresses the merging of tree-level matrix elements and parton showers. For more details see [328] and references therein. tions of 0.5 (s c dw) and 2.0 (s c u p). Lastly, the effects of system-
atic variations of the parton shower tuning are estimated. In [301] groups of independent parameters have been combined to form five separate variations which are labelled VAR1 to VAR3 c. Of these five only VAR3 c, which corresponds to a downwards (p y 3 c dw) and upwards (p y 3 c u p) variation of the αS value, was found to have a non-negligible influence. The overall systematic uncertainty is now taken as the maximal deviations from nominal values for each varia- tion individually and adding them, in quadrature. Since this requires the production of six additional MC samples for every signal sample, only three mass points, chosen to cover all of the relevant mass spec- trum, have been considered for each signal model. The uncertainties are taken to be flat over the full mass range but are estimated individ- ually for ˜χ±1χ˜±1, ˜χ±1χ˜01 and ˜τ. The adopted systematic uncertainties
are listed in table5.18. Figure5.34shows the final uncertainty and deviations from the nominal MC yields through all six variations.
particle sys. unc.
Gluinos 15% Sbottoms 15% Stops 15% Staus 4% Charginos ˜χ±1χ˜±1 5% Charginos ˜χ1±χ˜01 7%
Table5.18: List of systematic uncertain- ties assigned to all particle types due to ISR modelling.
mass [GeV]
0 0.02 0.04 0.06 0.08 0.1 0.12Deviation/Uncertainty
qcup scup py3cup
qcdw scdw py3cdw syst. uncertainty 350 600 850 350 600 850 350 600 850 τ∼ 0 1 χ ± 1 χ ± 1 χ ± 1 χ
Figure 5.34: Relative deviations from nominal MC yields through varia- tions of generator parameters in Ma d- Gr a p h. See text for explanations on the nature of variations. The system- atic uncertainties due to the ISR mod- elling are taken to be flat over all mass points and are calculated by adding the largest deviations from each of the three up-down variations in quadra- ture individually for ˜χ±1χ˜±1 (left seg-
ment), ˜χ±1χ˜01 (middle segment) and ˜τ
(right segment). The final values are indicated by the dashed red horizontal lines. Only three mass points have been simulated for all scenarios.
As a cross-check for the uncertainty onR-hadrons, the same vari- ations of the Ma dGr a p h generator parameters have also been ap- plied to a range of di-gluino samples. The deviations from nominal MC have been evaluated as described above and were found to be of similar size as the uncertainty derived from the efficiency difference between Py t h i a and Ma dGr a p h. Plots showing the deviations caused by individual parameter variations are given in figure E.4in appendix E.1.
5.8.5 Measurement of Pixel
dE/dx
The most probable values for the Pixel dE/dx distribution differ slightly in data and simulation. This is accounted for by the appli- cation of correction factors. A systematic uncertainty on thedE/dx modelling will be estimated by varying the correction factors by±1σ
and treating the difference to the nominal value as uncertainty. Since the detector conditions change over time due to radiation damage26
, the measured ionisation varies as well. A run-by-run
26
this is especially relevant for the IBL which only started data-taking in2015 and effects of radiation damage to the detector are clearly discernible.
correction of the measureddE/dx is performed accordingly, which derives scale factors to create a constant ionisation measurements throughout data taking. Small changes of condition within a run are taken care of by a 2% smearing of thedE/dxsignal27
.
27
The 2% are chosen since it reflects the maximal difference in scale factors of
two consecutive runs. Lastly, the dE/dxestimation in simulation contains an η correc- tion. A systematic uncertainty on this correction will be estimated by comparing the proton, pion and kaon mass peaks at a fixed low- momentum value as a function of η after the correction has been
applied.
The combined systematic uncertainty on the Pixel dE/dx mea- surement is expected to be small28
.
28
For comparison, the uncertainty de- rived for the MS-agnostic analysis per- formed with the 2015 data was < 3% [1].
5.8.6
β
estimation in signal MC
0.92 0.94 0.96 0.98 1 1.02 1.04 1.06 1.08 β MDT 0 10 20 30 40 50 60 3 − 10 ×Normalised number of muons
= 13 TeV
s Data
MC nominal MC up MC down
Figure 5.35: MDT β distribution for muons from data and simulatedZ →
µµdecays for nominal and scaled MC, in which the smearing has been in- creased and decreased by 5%. The scaled MC distributions bracket the data distribution as intended.
sys. unc. [%]
RPC RPC
particle MDT Corr. thres. Gluinos 0.0–0.7 0.1–2.0 0.0–0.6 Sbottoms 0.1–0.8 0.3–1.6 0.2–1.3 Stops 0.2–0.9 0.3–1.9 0.1–0.7 Staus 0.2–2.2 0.9–3.1 0.1–1.0 Charginos 0.1–2.5 0.3–2.7 0.1–0.7 Table5.19: List of systematic uncertain- ties assigned to all particle types due to the MSβestimation.
Simulated timing undergoes dedicated treatments to generate an agreement with the data distribution. For MDTs this is done by smearing the hit times as described in chapter4.9. To estimate the influence of a systematic error within the procedure, the derived smearing constants are varied by 10% up and down to create two new β distributions which encapsulate the data distribution. The
event and candidate selections are then repeated with the alteredβ
values and the effect on the signal yield within the signal regions are evaluated. The largest deviation from the nominal yield is taken as systematic uncertainty. Figure5.35shows the nominal, up- and down-scaledβdistributions for muons from simulatedZ→µµde-
cays compared to muons from data. A summary of the thus derived systematic uncertainties are included in table5.19.
No smearing is performed for RPCs. Instead some strips with wrongly modelled timing behaviour are corrected as was detailed in chapter4.10. Two sources of systematic errors are investigated. First, the RPC β is re-evaluated without the correction and the relative
difference in signal yield with respect to the correctly treated RPC
βis taken as systematic uncertainty. The second effect is the choice
of threshold values for the corrections to become active. Recall that only a subset of strips was affected by the mismodelling and that only those strips with resolutionσt0 > 2.1 ns and mean ¯t0 > 1.0 ns
are corrected. The choice of these threshold parameters were taken as the local minima in the corresponding distributions (see figures4.60
and4.61) but remain somewhat arbitrary. To consider the effect of the threshold choices the treatment is repeated with changed thresholds
σt0 > 1.8 and ¯t0 > 0.8 (MC down) andσt0 > 2.4 and ¯t0 > 1.2 (MC
up). The effect of all variations on the RPCβdistribution can be seen
from figure5.36. The uncertainties for all particle types are listed in table5.19. 0.92 0.94 0.96 0.98 1 1.02 1.04 1.06 1.08 β RPC 0 10 20 30 40 50 60 70 80 3 − 10 ×
Normalised number of muons
= 13 TeV s Data MC nominal MC raw 0.92 0.94 0.96 0.98 1 1.02 1.04 1.06 1.08 β RPC 0 10 20 30 40 50 60 70 80 3 − 10 ×
Normalised number of muons
= 13 TeV
s Data
MC nominal MC up MC down
Figure 5.36: RPC β distributions for muons from data and simulated Z →
µµ decays to estimate systematic un-
certainties. The left plot compares the fully treatedβdistribution with the dis- tribution for which no strip correction was performed. In the right plot the threshold values for the correction to become active have been altered to gen- erate two distributions roughly bracket- ing the data distribution. The final sys- tematic uncertainty is estimated as the largest relative change in signal yield with respect to nominal MC.
Since the Tile Calorimeter MC treatment also involves the smear- ing of simulated timing information, a systematic uncertainty is esti- mated analogously to the MDTs through a 5% up and down scaling of the smearing. The final uncertainties are given in table5.20.
TileCalβ]
sys. unc. [%] particle MS agnostic full detector Gluinos 0.4–3.2 0.5–4.5 Sbottoms 0.5–1.0 1.0–1.4 Stops 0.4–1.4 1.2–2.3
Staus 0.1–0.6
Charginos 0.1–1.5
Table5.20: List of systematic uncertain- ties assigned to all particle types due to the TileCalβestimation.
5.8.7 M
UG
I R LS
TAUreconstruction efficiency
To assess whether the reconstruction efficiency of the MuGi r lStau algorithm is the same in data and simulation, Z → µµ decays are
evaluated in data using a standard muon reconstruction algorithm29
. 29
The reconstruction efficiency of this standard algorithm is the same in data and MC as simulation was tuned for this algorithm.
The estimation thus rests on two basic assumptions: the reconstruc- tion efficiency of MuGi r lStau is the same for muons and slow particles and the MuGi r lStauefficiency for particles reconstructed with the standard reconstruction algorithm is the same as the true MuGi r lStaureconstruction efficiency. Both assumptions have been tested and were found to be fulfilled.
2.5 − −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 2.5 0.4 0.6 0.8 1 Efficiency Data MC = 13 TeV s 2.5 − −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 2.5 η 0.9 1 1.1 1.2 Data / MC
Figure 5.37: Reconstruction efficiency of the MuGi r lStaualgorithm in data and MC as a function of η. The effi-
ciencies have been measured inZ→µµ
events with respect to tracks identified by the standard reconstruction algo- rithm. Figure adapted from M. Habe- dank.
A data sample consisting of Z →µµdecays is selected using the
standard reconstruction algorithm. The efficiency of MuGi r lStau is evaluated in this sample and compared to simulation. Figure5.37 shows a comparison of the efficiencies as function ofη. The discrep-
ancies peak at about 15−20% in the region 1.2 < |η| < 1.6 where
simulation underestimates the efficiency in data. The difference is taken to be 10%, which results in systematic uncertainties ranging from 0.2% to 5.8% on the signal yield.
5.8.8 Pileup
All simulated samples have been reweighted to get agreement with the pileup profile in data. This includes a scale factor of 1/1.09
on simulated events which was determined by a dedicated ATLAS study group30
. To estimate the influence of the pileup modelling on
30
The purpose of the scale factor is to account for the incorrect total scatter- ing cross-section which was assumed in simulation.
the signal yield, the scale factor is varied up and down to values 1 and 1/1.18. The change with respect to nominal is taken as a sys- tematic uncertainty and ranges from 0.1% to 5.5% (0.2% to 3.8%) for the full-detector (MS-agnostic) analysis.