2.8.1 SCT data quality and performance
Data Quality needs to be optimised during operations. The SCT has its own monitoring tool developed as an analysis software algorithm that can be run both online and offline.
• Online: by running the full track reconstruction it ensures tracking and DAQ quality is within the accepted range. It also allows for rapid investigation of problems during data taking.
• Offline: the monitoring tools ensure that, after every run, only the portion of data that satisfy strict quality criteria are selected.
This quality assessment is done by monitoring track quantities including track parameters, number of reconstructed vertices, number of tracks associated to reconstructed vertices and hit-map distributions and track. Strict quality cuts are applied based on that information and data
Experimental Setup 41
are precluded from analysis in luminosity blocks corresponding to periods of 2 minutes of data taking on average. Each defect in tracking has a correspondence with a detector defect assigned when a portion of modules are unable to deliver reliable data. A detector defect is set if0.1% of the modules are unable to deliver good quality data based on error and noise rates.
The intrinsic hit efficiency is among the tracking parameters constantly monitored:
whereNhits is the number of hits on any given track (with transverse momentum higher than 1 GeV) andNholesis the number of holes on each track. A hole on a track is defined as an intersection of the track trajectory with an active detector element where no hit is found recorded . The intrinsic hit efficiency for 2012 is shown in Fig. 2.15. For an average hit efficiency lower than 99.5% in a region, either barrel or end-cap, data are not cleared for analysis. As expected, a small decrease in the hit efficiency was observed in 2012 with respect to early data taking. The higher track multiplicity, due to the increase of the average number of collisions per bunch crossing increases the probability of tracks sharing hits thus reducing the efficiency artificially. In nominal data taking the SCT hit efficiency was above99.7%.
line, given the peculiar conditions of its onset. No sign of this behaviour is seen in the Hamamatsu modules.
In the 2012 data the charged track density is much larger than the corresponding data in 2011 and 2010. In particular, the proton-proton collisions, with more than 30 interactions per beam crossing, start to approach the track density of non-central heavy ion collisions. Depending on the instantaneous luminosity and on the Physics trigger, the space average of the SCT hit occupancy in p-p collisions peaks to about 2%
per bunch crossing. The average hit efficiency for combined SCT tracks is 99.6%, but this number depends on the total number of tracks in the event and on the details of the pattern recognition algorithm, which do not assign hits to more than one track. The efficiency per barrel layer is shown in Fig. 3. The inner tracker alignment issues have been reported elsewhere in these proceedings .
VII. RADIATION DAMAGE
For the barrel region the SCT reverse current is in very good agreement with the calculations based on the luminosity profile and the temperature history. For the end-cap modules the agreement is less good but still within 25%. The 2012 data are being analysed. The SCT is still far from reaching the type inversion. At the present delivered luminosity the radiation damage can be displayed on-line from fill to fill.
Even during the same data taking period the silicon detector current displays a clear increase, due to radiation damage.
The beneficial annealing during technical stops are also clearly evident from on-line data.
The ATLAS Semi Conductor Tracker is taking excellent data, with ≥ 99.5% hit efficiency, 99.3% of the data have an absolute SCT coverage larger than 98.2% . The main reasons for inefficiencies were presented, together with some highlights on operating difficulties, which we had to cope with in 2012. The radiation damage of the SCT is closely following the expected profile. The LHC will stop operations for maintenance and for energy upgrade to 14 TeV in February 2013, for about two years. During this time the SCT will upgrade the data acquisition system to cope with higher occupancy for a collision pile up of up to 80 events per crossing.
 The ATLAS Collaboration, JINST 3 S08003, (2008)
 M. Moreno Llacer, ATL-INDET-PROC-2011-032 presented at IEEE-NSS, Valencia, (2011).
 A. Ahmad, Z. Albrechtskirchinger, P. P. Allport, J. Alonso, L. Andricek, R. J. Apsimon, A. J. Barr and R. L. Bateset al., Nucl. Instrum. Meth. A 578, 98 (2007).
 A. Abdesselam et al., JINST 2 P09003 (2007).
 F. Campabadal, C. Fleta, M. Key, M. Lozano, C. Martinez, G. Pellegrini, J. M. Rafi and M. Ullanet al., Nucl. Instrum. Meth. A552, 292 (2005).
 D. Dobos et al. [RD42, ATLAS Diamond Pixel Upgrade and ATLAS Beam Conditions Monitor Collaboration], Nucl. Instrum. Meth. A628, 246 (2011).
Fig. 1. The ATLAS inner detector. The SCT is located between the Silicon Pixel detector and the Transition Radiation Tracker.
20/04 20/05 19/06 19/07 18/08 17/09 17/10
SCT turn-on time (s)
Fig. 2. SCT turn-on time for 2012 running. For each period of stable beam we plot the difference between the time when stable beam is declared and the time when the SCT is reported to be ON by the detector control system.
The outliers, with a response time larger than 100 s, are 8% of the total and correspond either to fills with special set-up, or to fills where one or more beam background conditions were not initially met.
0 inner 0 outer 1 inner 1 outer 2 inner 2 outer 3 inner 3 outer
SCT Hit Efficiency
Fig. 3. Hit efficiency, as a function of the sensor layer in the SCT barrel for combined inner detector tracks in one run (n. 206573) taken in 2012
 A. Abdesselamet al., JINST 3 P02007, (2008).
 F. G. Hartjes, Nucl. Instrum. Meth. A552, 168 (2005).
 P. Bruckman de Renstrom, ”Advanced Alignment of the ATLAS Tracking System”, IEEE-NSS-24-3, Anaheim, CA, (2012).
Figure 2.15: Intrinsic SCT hit efficiency from combined tracking for each side (inner or outer) of each barrel layer from a typical run (206573) in 2012 with√
s= 8TeV . Each track is required to have at least7 silicon hits.
The noise occupancy (NO), which is defined as the probability to record a hit only due to noise, is also a closely measured quantity both online and offline by the monitoring tool in empty bunches. Throughout all data taking, the SCT noise occupancy remained significantly lower than the design specification of NO<5×10−4.
A low data rejection was achieved as shown in Tab. 2.2. A slight decrease of the total data cleared for analysis is observed in2012with respect to2011due to the increase of issues, discussed in the above section, being related to the rise of the delivered luminosity by the LHC . Throughout all data taking periods the SCT collected and cleared a portion greater than 99% of the data delivered by the LHC .
Experimental Setup 42
60 70 80 90 100 110 120
Z candidates / 1 GeV
0 5000 10000 15000 20000 25000
30000 Spring 2011 alignment Summer 2011 alignment
µ MC µ Z A
ATLAS Preliminary = 7 TeV s Data 2011,
L dt = 0.70 fb-1
Figure 2.16: Invariant mass distribution ofZ →µµ decays , where the mass is reconstructed using track parameters from the Inner Detector. Ideal alignment performance based on simulations is compared to observed performance of data processed with spring 2011 alignment and data processed with updated alignment constants.
Alignment is performed with cosmic and collision data by minimising the χ2 of track hit residuals. For collision data, tracks with pT > 15 GeV are selected from a jet trigger in order to minimise multiple scattering effects. Particularly for high momentum tracks, where multiple scattering plays a less prominent role, remaining misalignment effects can be assessed by looking at hit residual distributions from the reconstructed invariant mass of well-known resonances (for example Z-boson decays). Excellent agreement was found in the residual distributions for both barrel and End Cap . The resolution in the Z → µ+µ− invariant mass distribution from tracks reconstructed with the full Inner Detector, which is shown in Fig. 2.16, is very close to the expectation from simulation.
2.8.2 Irradiation damage in the strip detector
Irradiation of silicon sensors results in damage in the silicon bulk and the dielectric layers, with main effects being the increase in leakage current of the sensor, the change in the effective doping concentration and a change in the inter-strip capacitance. A measurement of the leakage current during off beam periods was made, and then, under the assumption that all high voltage currents originate from the current in the silicon bulk, the measurements were normalised to Tref = 0◦C (common factor for all LHC experiments).
The measured current was found to be in agreement  with the Hamburg/Dortmund model simulated using FLUKA and including self annealing effects based on the different measured sensor temperatures. A conversion of the integrated luminosity for both√
s= 7 TeV and√
s= 8 TeV to 1MeV neutron equivalent fluence to each barrel layer was made from simulations on minimum bias events of protons colliding at√
s= 7TeV. The increase in the leakage current, shown in Fig. 2.17, was observed to be correlated with the increase in luminosity as expected and excellent agreement between data and predictions is observed over the three years of operations. This indicates that the observed HV currents are mostly due to bulk generation current and also that the leakage
Date )3 A/cmµC (o @ 0volI
1 10 102
int. lum. deliv.
7/012010 10/01 2010 1/01
2011 4/01 2011 7/01
2011 10/01 2011 1/01
2012 4/01 2012 7/01
2012 10/01 2012 1/01
B3 B4 B5 B6
model prediction m 1
010 20 30
) -1Int. Lum. Deliv. (fb
1 10 sensor temperature
Figure 2.17: Measured leakage current for four SCT barrel layers . The predicted leakage currents by the Hamburg /Dortmund model  are shown (solid lines) while the associated bands show the 1 σ statistical and systematic uncertainty. On the top of the plot the measured sensor temperatures are shown.
current modelling incorporating self-annealing effects are well applicable. Although a significant increase in leakage current is observed, the change in depletion voltage so far is negligible and the SCT remains far from type inversion.
Physics objects definition
Proton–proton collisions produce final states containing large amounts of collimated sprays of particles, as well as leptons and photons. The interaction of these particles with the detector produce energy deposits in the wide range of ATLAS subsystems. Hits in tracking chambers and energy deposits in calorimeters are the starting point for complex algorithms that build unified sets of objects corresponding to the underlying properties of the measured particles. This chapter explains how the responses from several subsystems are reconstructed into sets of physics objects.
The reconstructed objects often have a complex definition, however the precision with which they correlate to the underlying physics quantities must be determined. This is achieved with complex simulation programs, embedded with extremely detailed detector description. These programs have to give an accurate and complete description of all the processes involved in proton-proton collisions. The interaction with matter is simulated with theGEANT4 program.
Moreover, the bulk of pp collision-data delivered by the Large Hadron Collider (LHC) are dominated by low energy interactions of quantum chromodynamics, which are of no interest to a t¯tγ cross section measurement. Therefore, stringent selection requirements must be imposed to the collected data, in order to decrease the contribution from background processes.
At first, in Sec. 3.1, the data considered in the measurement of the t¯tγ cross section are described. Then, Sec. 3.2 and Sec. 3.3, explain how events fromt¯tγandt¯tproduction are simulated.
Section 3.4 describes the simulation of processes other thant¯tproduction.
Section 3.5 explains and defines what are the reconstructed quantities, in use by this measure-ment. The selection criteria upon them and their comparison with simulations is detailed from this point onwards. Finally Sec. 3.8 defines the observable quantities used to discriminate between prompt-like objects (electrons or photons) and hadrons.
The analysis presented in this thesis is based on the full ATLAS dataset collected during the year 2011. Only the portion of good data, expressed in terms of integrated luminosity L = datasetR
LLumidt , so-called lumi-blocks, taken with good data quality conditions as defined by
the Top Working Group 1are taken into account in the selection requirements. The selected lumi-blockscorrespond to an integrated luminosity ofL= 4.59±0.08 fb−1.
Up to17interactions per bunch crossing were recorded with a typical mean value lying between 8 and 9. Table 3.1 shows the run range for each portion of data considered (Period), the luminosity and the single lepton triggers used.
This analysis uses centrally-produced derived ROOT-formatted  and ordered in ensembles of data (n-tuples). The n-tuples are produced using the ATHENA reconstruction framework 
used by ATLAS. Data are divided into exclusive sets (streams) based on the trigger information used. Electron and Muon streams, triggered, respectively, by deposits in the Electromagnetic Calorimeter and in the Muon Spectrometer are used for the selection of signal candidate events.
TheJetTauEtMiss stream, containing events triggered by deposits in the Hadronic Calorimeter, is used for the derivation of background processes.
Period Run range L Electron trigger Muon trigger
B-D 177986 - 180481 176.2 EF_e20_medium EF_mu18
E - H 180614 - 184169 937.7 EF_e20_medium EF_mu18
I 185353 - 186493 333.2 EF_e20_medium EF_mu18
J 186516 - 186755 223.5 EF_e20_medium EF_mu18_medium
K 186873 - 187815 583.3 EF_e22_medium EF_mu18_medium
L - M 188902 - 191933 2 401.8 EF_e22vh_medium1 or EF_e45_medium1 EF_mu18_medium Table 3.1: Data sample used in this analysis. Data is split into Periods ranging from B to M.
The associate run-range, based on an unique run identifier, is shown under the column Run range.
The right-hand-most columns indicate the trigger, see Sec. 2.6, used for the electron and muon channels. The labelEFindicates that the trigger selection is applied at the Event Filter (EF) level while the the labeleXX(muXX) indicates the minimumET(pT) requirement (in GeV) for the trigger to fire. The labelsvand hindicate a variable threshold and the request of isolation requirements.
The labelmedium represents the identification criterion that is applied at EF level .