atlas note - university of pennsylvania

ATLAS NOTE

June 19, 2009

Inner Detector Alignment and Performance Monitoring

John Alison1, Jurg Beringer2, Jed Biesiada2, Ben Cooper4, Tobias Golling2,Beate Heinemann2,3, Weina Ji5, Kyle Stevenson4, Sara Strandberg2,3

Abstract

We describe the framework used for monitoring the alignmentof the ATLAS Inner De-tector during cosmics and collision data taking. The goal isto check the alignment contin-uously using the data from the Express Stream to ensure that the alignment is of sufficientquality to start the processing or reprocessings of the physics streams. A variety of programshave been designed for this purpose: properties and residuals of individual tracks, the beamspot, the well-known resonancesK0

s , J/ψ , ϒ, Z0 and highpT electrons are all used to assessthe quality of the Inner Detector alignment and performance. We describe the purpose andfunctionality of each of these programs and illustrate the sensitivity to the alignment usinga few example distributions.

1)University of Pennsylvania, Philadelphia2)Lawrence Berkeley National Laboratory, Berkeley3)UC Berkeley, Berkeley4)Queen Mary University London, London5)Lund University, Lund

1 Introduction

The ATLAS experiment aims at doing a full first processing of the physics stream within about 24h ofdata taking. This is an ambitious goal and can only be met if stringent monitoring programs are in placeto rigorously validate the data before starting this processing. Therefore the computing model foreseesan ”Express Stream” that contains about 10% of all ATLAS dataand is preselected on the triggers thatare needed to ensure that the detector and corresponding software perform as expected.

For the Inner Detector (ID) there are six monitoring applications run at Tier0 on the Express Stream:

• the Pixel, SCT and TRT subsystems each have a dedicated monitoring application

• theInDetGlobalMonitoring looks at basic quantities (e.g. noise, timing) and correlates thembetween ID subsystems

• the InDetAlignmentMonitoring andInDetPerformanceMonitoring applications are com-posed of programs that produce histograms of properties andresiduals of individual tracks, thebeam spot, the well-known resonancesK0

s → π+π−, J/ψ → µ+µ−, ϒ → µ+µ−, Z0 → µ+µ−,W → eνe and Z → e+e−. The aim is to test the alignment of the Inner Detector and to evalu-ate the overall performance for physics. In particularInDetAlignmentMonitoring is designedto validate the alignment constants and to decide if the alignment constants need to be updated.Comparisons can be done by anyone by overlaying histograms of different alignment sets.

The latter two applications are the subject of this note. We first describe what can go wrong and whatare the failure modes that we hope to catch. Then we go througheach of the monitoring applications,explain its primary objective, show the key histograms and show some examples from the CSC [1](Computing System Commissioning), FDR [2] (Final Dress Rehearsal, phases 1, 2, 2a, 2b, 2c), cosmicray data taken in fall 2008 and weak mode [3] samples of how it could look in case of a problem.

2 Motivation for the Monitoring

While we cannot foresee everything that could possibly go wrong we can anticipate a variety of problemsthat are likely to occur and can prepare for them.

Expected problems are:

• The wrong calibration or alignment constants are accidentally retrieved from or copied into thedatabase. Mistakes are bound to happen and have in fact happened already e.g. during FDR.

• A change in software version does some damage. Despite the rigorous software validation proce-dure it is possible that mistakes slip in unnoticed. These can be caught by the monitoring.

• So-called ”weak modes” are an intrinsic problem of any alignment procedure based on residuals.They correspond to coherent deformations of the detector that leave the residual distributions andhence the fitted tracksχ2/DOF unchanged. In this note we present distributions that are sensitiveto certain weak modes deformations, described in Section 2.2 below.

The decision which alignment set is used for the data reconstruction is based on the monitoringhistograms. In addition the monitoring will be used to test for time dependence, and it will serve toassess the need for realignment.

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2.1 Alignment of the Inner Detector

The task of the ATLAS alignment procedure is to determine theactual positions in space (six degreesof freedom) of the pixel, SCT and TRT modules1). The procedure is broken down in three steps: therelative alignment of the the three subdetectors (referredto as “L1”), the relative alignment of the detectorlayers (referred to as “L2”), and the relative alignment of all the modules (referred to as “L3”). Severaltrack-based alignment techniques are applied to cosmic raydata, CSC, FDR and weak mode MonteCarlo simulations of a misaligned Inner Detector. Both for the CSC and FDR Monte Carlo samples amisaligned Inner Detector geometry [4] was used which was designed to be as close as possible to thereal as-built experiment. The CSC alignment of this misaligned Inner Detector is referred to as “CSCfirst-pass alignment”. Various alignment strategies were applied in the FDR exercise, leading to various“FDR-alignments”.

2.2 Weak Mode Deformations

It is known that certain global deformations can be applied to a perfectly aligned Inner Detector that leavethe residual distributions and hence the fitted tracksχ2/DOF unchanged. Such deformations are knownas “weak modes”. It is possible that the alignment proceduremay not fully remove the misalignments,but instead settle on a geometry where residual weak mode deformations remain. Even with the verylarge number of tracks that will be available for the alignment of the ID, these weak mode deformationscould prove to be extremely hard to remove or even detect. They thus have the potential to be residualsystematic misalignments of the detector. Where these weakmodes result in biases in the parameters ofthe reconstructed tracks they clearly present a serious threat to the physics potential of the ID.

There have recently been produced a set of Inner Detector conditions database tags which can beused to override the ID module positions so as to apply a weak mode deformation. These are describedin detail in [3]. By running ID reconstruction using these database tags we can test the sensitivity of ourmonitoring to four different types of weak mode deformation; the Curl, Twist, Elliptical and Telescopedeformations. For each deformation there are two geometries that differ in the size of the misalignment;one geometry where the misalignments are very likely largerthan will be encountered in reality (labelled“XXXX-Large”) 2), and another geometry where we believe the deformation is ofof a size that couldremain as residual misalignment (labelled “XXXX-Residual”). For the Curl and Twist deformations,the “Residual” geometries have been produced by running theID alignment procedure on the “Large”geometries, whereas for the Elliptical and Telescope the “Residual” geometries are simply smaller mag-nitude variants of the “Large” deformations.

3 Monitoring Applications

3.1 Brief Introduction to the Monitoring Framework

Data Quality Monitoring (DQM) is an important and integral part of high-energy physics experiments.The Data Quality Monitoring Framework (DQMF) [5] provides functionality for automated analyses ofmonitoring data (predominantly in form of histograms) through user-defined algorithms. The processingof the data and filling of the histograms is distributed to many nodes.3) A well-defined sub-set of the his-

1)internal module deformations are not considered for the time being2)where XXXX stands for Curl, Twist, Elliptical or Telescope3)Typically at the end of a run the histograms are added and a post-processing step allows one to make an operation on the

final histograms, e.g. fitting or dividing histograms.

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tograms, referred to as shift histograms (in addition thereare expert and debug histograms), is displayedto the shifter together with the test results of the DQMF algorithms. The result of the tests are convertedto a DQ status flag [6] that can be either green (all is OK), yellow (expert intervention is needed) or red(data is seriously compromised and can not be used for physics analyses4)). Additional functionality isprovided to archive and display the time history of certain monitoring related observables, e.g. vs. runnumber. In addition to monitoring each run, histograms can be merged across runs allowing for mediumand long term monitoring.

3.2 Track Selection

In theIDAlignMonGenericTracks,IDAlignMonEfficiencies,IDAlignMonResiduals andIDAlignMonTrackSegments modules described below we monitor the alignment via a detailed studyof the properties of Inner Detector reconstructed tracks. In order to ensure consistency of the studies andalso to maximise the sensitivity to misalignments it is necessary to impose selection criteria on the tracksused. This track selection is achieved centrally through use of theInDetAlignmentMonitoring/TrackSelectionTool, which in turn implements theInDetDetailedTrackSelectorTool or InDetCosmicTrackSelectorTool. The tool itself can beconfigured to cut on a number of different variables. Below are listed the variables which we use todefine a track selection:

• Track pT , d0 andz0.

• Number of Pixel, SCT and TRT hits that have been used in the track fit (can be cut on indepen-dently).

• For cosmics one can make an “event phase” cut which ensures that all TRT hits used in the trackfit fall within the TRT readout window.

• For cosmics we can also make requirements on the number of hits in the top or bottom halves ofthe detector.

The track selection is configured through theInDetExample/share/InDetMonitoringAlignment.py job options file. Here one can find the ac-tual values of the cuts used for different data taking situations; single beam, cosmics or collisions. Forthe monitoring of cosmics data taking the track selection plays a particuarly important role since withoutany constraints the tracks are topologically very different to the tracks that the Inner Detector has beendesigned to reconstruct (i.e. tracks that originate from the beamline).

3.3 Generic Tracks

TheIDAlignMonGenericTracksmodule provides basic track-related distributions like theχ2/nd f , thetrack parameter distributions, or the number of hits per track in barrel and EC’s for the pixel, SCT andTRT detectors. Most of the distributions serve as sanity checks to make sure that there are no problemswith the tracking reconstruction.

4)The alignment monitoring will never return a red flag since misalignments can always be fixed in the reprocessing

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3.4 Cluster and Hit Efficiencies

In theIDAlignMonEfficienciesmodule the efficiency is measured to reconstruct a cluster inthe Pixelor SCT detectors. Due to timing constraints, a hole search inthe TRT is not currently run during recon-struction. Thus, the overall TRT efficiency cannot be calculated in theInDetAlignmentMonitoring.Instead, ratios of the different types of measurements in the TRT, precision hits, tube hits and outliers5),are formed that, while being only a crude approximation of the efficiency, provide a quantity sensitive todetector misalignments.

The principle of the method is simple: the trace of a charged particle is reconstructed in the wholeInner Detector. In the Pixel and SCT this reconstructed track is extrapolated to a detector layer. Wheneverthe extrapolation traverses a sensitive detector element areconstructed cluster is expected. This numberof expected clusters is compared with the number of reconstructed clusters. For the TRT, the number ofprecision hits, tube hits, and outliers, are compared to thetotal number of measurements.

Misalignments result in inefficiencies, but inefficienciesalso arise from non-functional and disabledmodules, dead front-end chips and dead or noisy pixels, strips or straws. In the efficiency definition inthe following disabled modules are not counted as sensitivedetector elements and thus do not contributeto the inefficiency. This is not yet the case for front-end chips and masked off pixels or SCT strips. Theefficiency is defined as the number of clusters attached to a track divided by the number of expectedclusters from the track extrapolation, which can be interpreted as the combined efficiency to find and toattach a cluster to a track.6) Cluster efficiencies and the ratios of the TRT measurements are presentedvs. layer number,pT , η , φ and per module.

The Pixel cluster efficiency is shown for the cosmic data as a function of barrel layer in Fig. 1(a)before and after the alignment. The ratio of precision hits to total measurements as a function of strawlayer in the TRT is shown in Figure 1(b) for cosmic-ray data, both before and after the TRT alignment.The sensitivity of this plot to misalignment is clearly seenas a drop in the percentage of precision hitsbefore the TRT alignment.

3.5 Track Segments

TheIDAlignMonTrackSegments module compares track parameters of different track fits to agivencharged particle’s trajectory. The aim of this module is to detect relative ID sub-detector misalign-ments and systematic misalignments within a sub-detector.Input tracks that are fit in different ways arematched and their properties compared. By analyzing tracksfit only with measurements from a partic-ular sub-detector to those fit with only measurements in another, the relative sub-detector misalignmentcan be probed. For example, tracks crossing the entire ID canbe fit once using only Pixel and SCTmeasurements, and again using only TRT measurements. Discrepancies in the resulting track fits indi-cate a relative TRT-Si misalignment or an internal inconsistency in either the SCT-Pixel or TRT internalalignment.

The large number of cosmic rays that have been recorded to commission the ATLAS detector haveprovided a unique class of tracks that theIDAlignMonTrackSegments module can exploit. Unliketracks from collision events, cosmic-ray muons can traverse the entire ID barrel, leaving hits in both the

5)A measurement in the TRT can be of three types: precision hits, tube hits, or outliers. Precision hits are used in the trackfitand include timing information. Tube hits are a class of outliers which are used in the track fit, but ignore timing information.Outliers are more discrepant with the track fit than tube hitsand are not used in the track fit.

6)A track on a detector element can have three different states: a reconstructed cluster is found which is attached to the track,a reconstructed cluster is found which is not attached to thetrack (so-called outlier), no reconstructed cluster is found (so-calledhole).

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Figure 1: Histograms produced by running theIDAlignMonEfficienciesmodule on cosmic-ray data.

upper and lower halves of the ID. These tracks can be split near the interaction point and fit separately,resulting in two collision-like tracks that should have thesame track parameters. These tracks can thenbe compared with theIDAlignMonTrackSegmentsmodule. Here again, differences in track parameterscan be used to monitor the ID alignment. In this context theIDAlignMonTrackSegments module alsoprovides a natural setting for measuring the ID tracking performance and track parameter resolution.

The output of theIDAlignMonTrackSegments module is histograms sensitive to detector mis-alignment. These histograms come in two varieties: one dimensional histograms that plot the differ-ence in tracking properties and two dimensional profiles that plot these differences as a function ofthe track parameters. The histograms are made for all tracks, and separately for positively and neg-atively charged tracks. Differences in track parameters that depend on the charge of the track canindicate the presence of systematic deformations in the ID [3]. All track selection is done throughthe toolIDAlignMonTrackSelectionTool, where the two track collections to be compared are de-fined7). Tracks passing the selection requirements are passed toIDAlignMonTrackSegments whichthen matches tracks from the two input collections8) , compares their track parameters, and fills the his-tograms.

The first type of histograms include the differences of the five track parameters,d0, z0, η0, φ0, andqpT

, as well as the difference in the charge of the tracks and in the number of hits in the three ID sub-detectors. Two examples are shown in Fig. 2(a)-2(b) which plot the difference inφ0 andd0, respectively,of the upper and lower halves of tracks reconstructed in cosmic data. The distribution is shown fortracks reconstructed both before and after a relative SCT-TRT alignment (“L1”). The sensitivity of thedistribution to misalignment can be seen both in the shifting of the mean from zero, and in the wideningof the peak in the misaligned case. Fitting tracks with a misaligned geometry can result in a bias inφ0

and a degradation in the resolution ofφ0.

In the second type of histograms created byIDAlignMonTrackSegments, the tracking differences

7)For cosmic tracks split in upper and lower halves there is an option to create the track collections compared by internallycallingInDetTrackSplitterTool from one input track collection configured byIDAlignMonTrackSelectionTool.

8)If there is a track in the second input collection within a specified ∆R range of a track in the first input collection, the pairsare matched. The closest such pair is chosen if multiple tracks fall withing the∆R range.

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(b) Difference ind0 of tracks reconstructed in the pixel and SCTdetectors from cosmic ray data and Monte Carlo simulation which aresplit into upper and lower halves. Tracks that were split were requiredto go through the innermost layer (d0 < 50 mm andz0 < 400 mm), andhave a minimumpT of 2 GeV. The black distribution (open squares)is before the cosmic silicon alignment and blue (filled circles) is after;the red distribution (open circles) is for Monte Carlo simulation witha perfect knowledge of the alignment.

Figure 2: Histograms produced by running theIDAlignMonTrackSegments module on cosmic-raydata.

given above are plotted as functions of the track parameters. For each track parameter,Px, five plots ofthe form∆Px vs Py are created9), where∆Px indicates the track parameter difference of the input tracksandPy represents one of the five track parameters. An example of these plots is given in Figure 3, wherethe difference ind0 of upper and lower cosmic tracks is plotted as a function of thed0 of the upper track.The plot is again made from cosmic data and is shown for tracksreconstructed both before (black opencircles) and after (blue filled circles) a realtive SCT-TRT alignment (“L1”). The distributions sensitivityto misalignment is seen as a biasing of the difference ind0 before the alignment. After the TRT alignmentthe difference ind0 has improved for all values ofd0. Distributions of this type can not only be used todetect an ID alignment problem, but can provide insight intothe nature of the misalignment.

TheIDAlignMonTrackSegmentsmodule has proved to be a valuable tool for debugging and vali-dating the ID alignment with cosmic data. With the collisiondata with which the ID will be further com-missioned, theIDAlignMonTrackSegmentswill provide a means of assessing the relative sub-detectoralignment and serve as a probe for identifying coherent detector distortions.

3.6 Residuals

A tracking residual is defined at each detector surface whichcontains a measurement used in a trackfit. This residual is defined as the distance between the extrapolated track position at that surface andthe corresponding measurement position. Residuals are calculated in the local reference frame10) of

9)These second type of plots are created during the post-processing stage from other intermediate histograms that keep therelevant information.

10)Note that what is actually used is the “measurement” frame, which is generally but not always equivalent to the local frame.

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Figure 3: Difference ind0 vs thed0 of upper track from tracks in cosmic ray data which are split intoupper and lower halves. Tracks that were split were requiredto have at least 2 Pixel hits, 9 SCT hits, 45TRT hits, and have a minimumpT of 2 GeV. The distribution of black, open circles is before the TRTalignment and the blue filled circles is the distribution after.

the detector element, and are given as components in local X and local Y 11). Residuals provide thecontribution of a particular detector element or region to the χ2/DOF of the track fits, and are thusan important quantity for alignment monitoring purposes. Alarge bias in the residual distribution ofa particular detector element can indicate that this element is misaligned with respect to the rest ofthe Inner Detector. The ID alignment algorithms rely on minimization of the tracking residuals, thuswe do not expect large residual biases after reconstructingtracks with corrected alignment constants.Nevertheless, residuals must be monitored to ensure that this expectation is met; that the alignmentprocedure, including successful uploading of the correct alignment constants to the database, has notfallen short. These residual distributions will be continuously monitored and changes could indicate ashift of the ID detector elements, which requires the ID alignment procedure to be repeated.

The monitoring of tracking residual and pull distributionsin the Pixel, SCT and TRT is part oftheInDetAlignmentMonitoring package and is handled by theIDAlignMonResiduals module. Inthis module 1-D, 2-D and 3-D histograms are filled with the residual and pull values as a function ofdetector module identifier. Tracks passing selection requirements (see Section 3.2) are looped over andthe residual and pull histograms are filled for each hit12). All ID residuals and pulls are calculatedusing theTrkValTools/ResidualPullCalculator tool, and are fully unbiased; the hit for which theresidual is being calculated is first removed from the track and the track re-extrapolated through thesurface13).

A very large number of histograms are filled by theIDAlignMonResiduals module such that itis possible to examine the residual and pull distributions for each individual detector module/phi-sectorin the Pixel, SCT and TRT. However, clearly such a large number of histograms cannot be monitored

11)In the TRT and SCT residuals are only defined in the local X direction since these detector elements do not provide anydirect measurement in the local Y direction. In the future weplan to monitor SCT Y residuals calculated using the SCT stereoangle information.

12)here each mean each hit which is flagged as a measurement (i.e.has Trk::TrackStateOnSurface::Measurement)13)In the SCT both of the hits that correspond to the two sides of the module are removed from the track in this way.

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offline. Thus the histograms filled are subsequently “post-processed”, as described in Section 3.1, toform more concise histograms suitable for monitoring purposes. Instead of displaying the distribution ofresidual or pull values, these histograms display the mean or width of the residual or pull distributions,defined using a 2-stage Gaussian fit14). Three different types of histograms are filled in this way:

• 1-D plots of the residual/pull mean and width as a function ofthe layer (disk) in the Pixel, SCTand TRT barrel (endcap).

• 1-D plots of the residual/pull mean and width as a function ofthe moduleη (ring) or φ (stave)identifier within a particular barrel layer or endcap disk ofthe Pixel/SCT. For the TRT we plot asa function of the moduleφ sector.

• 2-D plots of the residual/pull mean and width as a function ofthe moduleη −φ identifier withina particular barrel layer or endcap disk of the Pixel/SCT.

In order to reduce the number of histograms that need to be monitored the plots of the residual/pull asa function of module stave/ring for each layer are combined into one with a gap of 10 empty x-axisbins separating them. Thus for these plots the module-η /φ identifier as read from the x-axis no longercorresponds to the actualη /φ identifier. The 1-D plots are monitored by the offline shiftervia the DQMFframework (see Section 3.1). The DQMF algorithms that are applied differ for the residual mean andresidual width histograms. For the residual mean histograms we test whether or not the histogram binsare consistent with zero (the value one would expect for a perfectly aligned detector). If all bins areconsistent with zero the histogram will be flagged as green. If there are bins which are not consistentwith zero the histogram will be flagged as yellow or red, depending on the number of bins that failed.For the residual width histograms we will eventually compare each bin to a reference histogram to checkthat the widths have not changed. However, since in early data taking such a reference is likely to bepoorly defined we will initially apply a much more simplistictest such as requiring all the bins to containentries.

Figure 4 shows the residualx distribution in the pixel barrel detector for tracks reconstructed in thepixel and SCT detectors from cosmic ray data before and afteraligning the detector, and in comparisonwith Monte Carlo simulation with a perfect knowledge of the alignment.

In Fig. 5, 6 and 7 we show some examples of the Pixel and SCT histograms that are monitored.These plots have been produced by runningInDetAlignmentMonitoring on a Z→ µµ sample thathas been reconstructed using module positions that are identical to those used in the simulation (per-fect alignment), and also using the Curl and Elliptical systematic deformations that are described inSection 2.2. In Fig. 5 the residual mean as a function of silicon barrel layer for the perfect alignmentcase is compared to that for the Curl-Residual systematic deformations. Since the Curl deformation ap-proximates well to a weak mode the residual distributions are largely unbiased and consistent with theperfect alignment. However, in Fig. 5(b) one can see the limitations of this weak mode approximationthe barrel-endcap overlap region. In Fig. 6 we compare the perfect alignment case to the Elliptical-Largesystematic deformation, both before and after the alignment procedure has been run on the deforma-tion. The plots show a clear sinusoidal structure in the silicon residual mean distribution as a functionof module-φ identifier after the alignment has been run on the Elliptical-Large deformation. Since theseresidual distributions are those of tracks that have been fitted with both silicon and TRT hits, this struc-ture is indicative of some global misalignment in the X-Y plane of the entire silicon detector relative tothe TRT. In Fig. 7 the residual mean and width map for the 4th SCT barrel layer is shown for the aligned

14)First a Gaussian fit is made in the range [MEAN + 2*RMS, MEAN - 2*RMS], then a second Gaussian fit is performed inthe range [MEAN + 2*WIDTH, MEAN - 2*WIDTH], using the mean andwidth from the first fit.

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Figure 4: Residualx distribution in the pixel barrel detector for tracks reconstructed in the pixel andSCT detectors from cosmic ray data and Monte Carlo simulation. Tracks are required to go throughthe innermost layer (d0 < 50 mm andz0 < 400 mm), and have a minimumpT of 2 GeV. The blackdistribution (open squares) is before the cosmic silicon alignment and blue (filled circles) is after; the reddistribution (open circles) is for Monte Carlo simulation with a perfect knowledge of the alignment.

Elliptical-Large reconstruction. The same residual bias structure as a function of moduleφ can be seen,whilst the distribution of residual widths remains relatively flat.

Figures 8(a) and 8(b) show two examples of the monitored residual histograms for the TRT. Theyare produced by running theInDetAlignmentMonitoring on cosmic-ray data. Figure 8(a) shows theaverage residual for all straws in a TRT barrel plotted as a function of φ sector. Figure 8(b) shows thecorresponding residual RMS as a function ofφ sector. In both cases the distributions are reproduced threetimes with different detector alignments. The distributions are shown before the TRT alignment in black(triangles), after a relative SCT-TRT alignment (“L1”) in red (squares), and after an additional internal“L2” TRT alignment in blue (cicles). The sensitivity to detector misalignment in Fig. 8(a) is seen as abiasing of the average residual from zero before the alignment in black (triangles), which is then reducedin blue (cicles) and red (squares) distributions. In Fig. 8(b) the effect of the detector misalignment isseen as an increase in residual RMS before the alignment. Additional TRT specific plots included in theIDAlignMonResidualsmodule are listed below.

3.7 Beam Spot Monitoring

3.7.1 Introduction

Monitoring of the beam spot as part of alignment monitoring allows to check that the beam spot was cor-rectly determined, the corresponding information properly uploaded to the conditions database (COOL),and that there are no unexpected changes in the beam spot parameters (position, width and tilt angles).Changes in observed beam spot parameters may occur both as a result of physical effects such as changes

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(a) X Residual mean as a function of layer in the silicon bar-rel.

(b) X Residual mean as a function of disk in the silicon end-cap A.

Figure 5: Examples of monitoring residual means as a function of layer in the silicon barrel and endcaps.The black solid points are for ideal geometry and the red openpoints for the Curl-Residual systematicdeformations. Tracks including silicon and TRT hits are used.

of the LHC machine parameters settings, ground motion or thermal drift that result in a physical shiftof the beam spot, as well as due to changes in alignment constants or software problems. For example,changes in the center–of–gravity correction, which corresponds to a global translation of the coordinatesystem, will directly lead to a corresponding apparent shift of the beam spot position even though thephysical position of the beam spot may not have changed.

At the nominal LHC operating parameters, the beam spot in ATLAS can be represented to a goodapproximation by a 3-dimensional Gaussian with a transverse widthσx,y = 12µm and a longitudinal sizeσz = 5.3cm (at the nominal operating parameters we haveβ ∗ ≫ σz and the “hourglass effect” [7] isnegligible). Thus monitoring of the beam spot position is primarily useful in the transverse plane. A tiltof the LHC beam spot with respect to the z–axis of the detectorcan arise both due to a physical rotationor due to residual misalignment.

Two methods for monitoring the beam spot are implemented in moduleInDetAlignMonBeamSpotof theInDetAlignmentMonitoring application, corresponding to the two primary methods for deter-mining the beam spot based on tracks and on primary vertices,respectively.

3.7.2 Beam Spot Monitoring Based on Tracks

Monitoring of the beam spot based on individual tracks makesuse of the correlation between the impactparameterd0 and the azimuthal angleϕ in the transverse plane. For a track coming from a vertex at(x0,y0), the impact parameter (with respect to(0,0)) is given by

d0 = −x0 sinϕ + y0cosϕ (1)

A scatter plot ofd0 vsϕ for (mostly) primary tracks will therefore show a sine-curve like structure unlessd0 is calculated with respect to the beam spot position. This isillustrated in Fig. 9 – 11. The amplitudeof the curve corresponds to the distance from (0,0) to the beam spot; the phase of the curve indicates the

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(a) X Residual mean as a function of module-φ identifier inthe Pixel barrel.

(b) X Residual mean as a function of module-η identifier inthe Pixel barrel.

(c) X Residual mean as a function of module-φ identifier inthe SCT barrel.

(d) X Residual mean as a function of module-η identifier inthe SCT barrel.

Figure 6: Examples of monitoring residual means as a function of the module-η /φ identifier in the siliconbarrel layers. The black solid points are for ideal geometryand the red open points for the Elliptical-Large systematic deformation after alignment of the silicon only. Tracks including silicon and TRT hitsare used. The clearφ dependent structure in the red open points indicates a global misalignment of thesilicon with respect to the rest of the Inner Detector that has been introduced by the alignment procedure.

azimuthal angle to the beam spot. When calculated with respect to the correct beam spot as in Fig. 10,the width of the curve is a measure of the transverse beam spotsize convoluted with the impact parameterresolution.

By default, the track-based beam spot monitoring plots are made from theTrackParticleCandidatecollection using only tracks withpT > 1.5GeV (additional track quality cuts will likely be added in thefuture).

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Figure 7: Examples of monitoring residual means and widths as a 2-D residual “map” of an entire layerin the silicon barrel. Here we see the 4th SCT layer for tracksreconstructed with the aligned Elliptical-Large systematic deformation. The clearφ dependent structure indicates a global misalignment of thesilicon with respect to the rest of the Inner Detector that has been introduced by the alignment procedure.

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Figure 8: Example of TRT residual plots from cosmic-ray data. Tracks were required to have> 2 Pixelhits,> 9 SCT hits,> 45 TRT hits, and havepT > 2 GeV.

3.7.3 Beam Spot Monitoring Based on Primary Vertices

The distribution of primary vertices is given by the beam spot profile convoluted with the primary vertexresolution. The monitoring plots in theInDetAlignMonBeamSpotmodule include both one dimensionalprojections and two dimensional scatter plots of the primary vertex position, as well as plots of thenumber of tracks,χ2 per degree of freedom of the primary vertex fit, error on the vertex position, andplots of thepT andη distribution of tracks used in the primary vertex fits (some of these plots are onlyavailable as expert-level plots). In each event, only the vertex flagged as the primary one (currently basedon the highest sum ofp2

T ) is used, and is furthermore required to include at least 10 tracks in its vertexfit. Some examples of these monitoring plots are shown in Fig.12 - 14.

By default, histograms of the transverse primary vertex position x andy are shown relative to theassumed beam spot position, using a small scale of±200µm. If the beam spot position is not correctlyknown, these histograms will typically be empty or have justa few events, immediately alerting a shifterthat there’s a problem with the beam spot being monitored.

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Figure 11: Example of ad0 vs ϕ distribution calculated with respect to the beam spot as in Fig. 10 butfor a case where the beam spot had not yet been correctly determined. The large structure immediatelyshows that the beam spot position is not correctly known.

Obviously plots of primary vertices are only meaningful forthe purpose of beam spot monitoringif they have been produced without constraining the primaryvertex to an assumed beam spot. Forthe primary vertex collection (VxPrimaryVertex) available from standard reconstruction this is onlythe case for the first processing of the express stream. Therefore this is the only time in the currentprocessing scheme where vertex-based beam spot monitoringplots are useful. In other processing steps,these monitoring plots are disabled.

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16

3.8 K0s → π+π− Decays

3.8.1 Introduction

This module is part ofInDetPerformanceMonitoring and aims to monitor the quality of the ID re-construction using the long-livedK0

S particles. TheK0S decay time is 90ps, which translates to an average

decay length of roughly 30cm for a 5GeVK0S , allowing radial tracking studies. In addition,K0

S ’s arecopiously produced in most triggered physics processes, and have a much larger momentum transferduring decay (roughly half the rest mass is available) than other long-lived particles such asΛ’s, with acorrespondingly larger sensitivity to reconstruction imperfections.K0

S ’s are reconstructed in theπ+π−

decay mode, which has a branching fraction of approximately68%. The simplicity of the reconstruc-tion is an additional benefit, as the reconstructed invariant mass corresponds closely to the reconstructedmomentum of the daughter tracks with respect to resolution and bias.

3.8.2 V 0 Reconstruction

V0’s are reconstructed using theV0Finder package. This modules selects oppositely charged tracks andattempts to fit them to a common decay vertex using theVKalVrt fitter. Combinations that do not failthe fit and various loose cuts are collected in theV0Candidates container. The package also computesthe track parameters at the decay vertex, extrapolating thetracks using the material and magnetic field inthe detector model.

3.8.3 K0S Selection

The following selection criteria are used to selectK0S candidates from theV0Candidates container,

focusing on selecting secondary vertices in the appropriate invariant-mass window and rejecting thedominant primary combinatoric background component:

• 400 MeV/c2 < Mπ+π− < 600 MeV/c2

• transverse decay length,Lxy > 12mm

• proper decay time,τ > 4ps

• vertex probability≥ 0.001%

• At least 3 SCT hits in each track

• Two-dimensional angle between the decay-vertex vector with respect to the origin and theK0S

momentum vector, cosθ > 0.998

3.8.4 Efficiency

The total efficiency, including acceptance and selection criteria, evaluated at the truth level on generatedK0

S ’s in simulated samples is at the 1-2% level, depending on thephysics process, for about 0.02 K0S per

event and a signal to background ratio of about 5 under the mass peak. This corresponds to about 1000reconstructedK0

S in a few hours running at a 10Hz trigger rate, corresponding to typical express-streamrates at early LHC luminosities of 1031 cm−2 s−1. Figure 15 shows the reconstructed invariant massdistribution for such a sample in simulated minimum-bias events, with a resolution of 7 Mev/c2. Thepeak is fitted with the sum of a gaussian and an exponential function, for the signal and backgroundcomponents, respectively.

17

Figure 15: Invariant-mass distribution ofπ+π− pairs in simulated minimum-bias events.

3.8.5 Monitoring Sensitivity

Preliminary studies show that the peak and width of the invariant-mass peak are sensitive to imperfectionsin the material parameterization and alignment of the reconstruction of Inner Detector tracks. Figure 16shows the resolution of the peak versusφ for several alignment algorithms used in the FDR2 exercise.Figure 17 shows the variation of the peak position versus radius, which is roughly correlated to extramaterial included in the simulation that was not included inthe reconstruction. (This correlation needsto be studied further to determine whether it is useful for monitoring.)

3.8.6 Monitoring

Based on the preceding preliminary studies, the offline ID performance monitoring package will includethe followingK0

S plots:

• Invariant Mass Distribution

• Mass-peak width versusη , φ , decay radius, andpT.

These plots will be monitored by data-quality shifters during data-taking. The number and com-position of the plots will be adjusted dynamically, depending on shifter load and observed sensitivity toreconstruction imperfections and errors. For example, mass-peak position versus the same variables, aswell as the raw candidate distributions versus these variables could also be included. All these plots, aswell as more detailed information is available in the expertsection of the package for additional analysisand debugging purposes.

The package was successfully used for online monitoring during the FDR2 exercise, and it is readyfor data-taking. As theJ/ψ module has observed sensitivity to the Curl-Large and Curl-Residual weak

18

Figure 16: Width of theπ+π− invariant-mass peak versusφ in simulated FDR2J/ψ events for “ideal”(black filled circles), “CSC first-pass” (green open squares), FDR2 “without L3” (blue open triangles),and FDR2 “with L3” (red open circles) sets of alignment used in the ID reconstruction algorithm.

Figure 17: Position of theπ+π− invariant-mass peak versus radius in 20,000 simulatedZ → e+e−

events with ideal alignment applied. This sample had extra material inserted in simulation, but not inreconstruction at several radial positions.

19

mode deformations in the distribution of the mass-peak position versus the curvature difference of thedaughter tracks, this quantity will also be monitored in future versions of theK0

S module. AsJ/ψ ’s andK0

S ’s have a similar distribution of∆R between the two tracks (due to the high-boost bias of the high-pT

J/ψ trigger), the two mass distributions could have similar sensitivity to this weak mode.

3.9 J/ψ → µ+µ− and ϒ → µ+µ−

3.9.1 Introduction

The heavy quarkonium production is expected to be so large that we are able to monitor their per-formance in detectors. Here we assess the Inner Detector alignment with theIDPerfMonJpsi andIDPerfMonUpsilon modules, using lowpT objects from di-muon decays ofJ/ψ and ϒ. The crosssections of both decays before trigger efficiency are estimated to be tens of nb in ATLAS which offergood statistics in the Express Stream. And the mass resolutions are very sensitive to detector effects sincethe natural mass widths are so small. Moreover, by plotting mass shifts for the reconstructed particlesversus different variables it is also possible to reveal theinfluence of misalignments on specific trackparameters.

3.9.2 Event Selections

A few criteria are used to selectJ/ψ or ϒ candidates:

• Combined muons are retrieved and their Inner Detector tracks are kept as monitoring objects.

• All possibleµ+µ− pairs passing the cutpT (µ) > 4 GeV are formed.

• The di-muon pair whose invariant mass falls within a window of 2 GeV around the nominalJ/ψor ϒ mass are retained.

3.9.3 Monitoring Strategy

Each module produces monitoring histograms containing an invariant mass distribution plot and a num-ber of dependency-plots depicting mass shifts or widths versus track parameters. As described inSec. 3.1, a post-processing step is applied after histograms merging in order to fill the dependency-plotsproperly.

After several tests during FDR-2 and follow-up exercises, shifter plots from theIDPerfMonJpsimodule and the corresponding DQMF tests are defined as:

• Invariant mass distribution. The DQMF test is set as checks of |Mean−3.097|, width andχ2/ndffrom a fit of gaussian plus one degree of polynomial distribution. The mass, yield and width ofselectedJ/ψ in each run are requested for a time history monitoring (see Sec. 3.1).

• Width of mass peak as a function of pT(J/ψ),η(J/ψ),φ(J/ψ),z0(µ),max(|η(µ)|),1/pT(µ) andcurvature difference between the daughter tracks. The DQMFtest is a loose comparison withreference.

More plots are saved as expert or debug histograms, for instance, the ones of mass shifts versus vari-ables. The shifter configuration is likely to be adjusted during data-taking depending on statistics andmonitoring sensitivity.

20

3.9.4 Sensitivity

Some examples ofIDPerfMonJpsi monitoring plots from the FDR-2 and FDR-2c exercises are givenbelow. TheJ/ψ invariant mass distributions from different Express Stream processings are presentedin Fig. 18. During FDR-2, there was one run of Express Stream reconstructed with the CSC first-passalignment [4]. The corresponding monitoring plot is presented in Fig. 18(a) and the width ofJ/ψ wasas good as expected. Meanwhile, several sets of alignment constants were derived containing differentlevels (described in Sec. 2.1). The alignment effects without L3 could be seen in Fig. 18(b) where thevariation of width was much larger. After including L3 alignment the mass distribution was improved,seen in Fig.18(c), although still not comparable to the one from the CSC fist-pass alignment because of abug in the FDR-2 alignment. Figure 18(d) was produced in FDR-2c with the alignment bug fixed so thewidth appeared to be recovered. The total number of observedsignal events in each run was estimatedaccording to the fit of the mass histogram. We conclude that from one run of Express Stream, at anintegrated luminosity of 36 nb−1, the module selects approximately 1000J/ψ → µ+µ− events and thewidth of J/ψ mass peak has been seen sensitive to alignments.

Some other shifter plots, like the width versuspT (J/ψ) andη(J/ψ), are shown in Fig. 19. Thereference were produced from one run of Express Stream with the CSC first-pass alignment.

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Figure 18: Invariant mass distribution of Di-muon (ID) tracks in different aligned Express Stream dur-ing FDR-2 and FDR-2c, fit with gaus+pol1 to separate signal and background. Values of the gaussianfunction at bins between 2.6 GeV and 3.6 GeV were summed for the total number of signal events,N(J/ψ).

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Because the FDR-2 Express Stream contains the triggermu6 Upsimumuwhich caused a too low rateof ϒ events, there were not sufficient signals observed fromIDPerfMonUpsilon module. However, thesame monitoring strategy has been setup as for theJ/ψ .

3.9.5 Weak Modes Study

We have also appliedIDPerfMonJpsi andIDPerfMonUpsilon modules to study the “weak modes”(see Sec. 2.2), seeking parameters sensitive to each mode. The Curl-Large/Residual and Twist-Large/Residualdeformations have been used to reconstruct aJ/ψ → µ+µ− sample with statistics corresponding to anintegrated luminosity of∼ 2.3 pb−1 and directϒ → µ+µ− sample of∼ 11 pb−1.

In Fig. 20, the directJ/ψ sample was reconstructed with three alignments: ideal alignment, Curl-Large and Curl-Residual. The invariant mass ofJ/ψ has no clear change while plot of mass shift versuscurvature difference reflects the Curl-Large deformation and subsequent alignment improvement in theCurl-Residual sample. That is what we expected from the Curldeformation since it produces a charge-dependent curvature bias [3]. And we could recognize the deformation even in the “Curl-Residual” case.The same work has also been done for twist mode, presented in Fig. 21. The Twist deformation couldbe seen in the mass shift versus pseudorapidity difference because it changes the track curvature as afunction of pseudorapidity.

Figures 22 and 23 show similar results for directϒ sample reconstructed using the Curl and Twistdeformations.

3.10 Z0 → µ+µ−

3.10.1 Introduction

TheZ0→ µ+µ− resonance is one of the main handles available to us in investigating the high PT trackingcapabilities of the ATLAS detector. A pure sample can be tagged independently of the the ATLAS innertracking system, with a reasonable efficiency. Given thatZ0 → µ+µ− events are produced at a rate of 600per pb−1 at 14 TeV ( 400 per pb−1 at 10 TeV) this provides a statisticly powerful tool for checking thealignment of high pT tracks. Since the results of the track fit procedure ( ref. ) are strongly correlated withmisalignments of the inner detector, kinematic biases willoften result from any residual miss-alignments.

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The invariant mass, and corresponding track parameters of the reconstructedZ0 events, were exam-ined for several systematically missaligned sets ( defined earlier ). Miss-alignment induced momentumasymmetries and invariant mass shifts with regards to the curvature asymmetry were found to be, inparticular, powerful indicators of systematic alignment biases.

3.10.2 Event Selection

TheZ0 → µ+µ− events were tagged in simulation using the ATLAS muon spectrometer using standardPT and muon isolation cuts. These are detailed below:

• Two high-pT muon spectrometer tracks both passing the cuts listed.

At least 12 MDT ( muon drift tube ) hits were required.

Require spectrometer pT > 20GeV/c, after calorimeter energy loss corrections applied.

Muon isolation requirement.

• Required the invariant mass of the tracks to lie in the region50 to 130GeV/c2

These cuts gave an overall event tag efficiency of 32.5% for a well aligned sample ( this does nottake into account any efficiency loss due to event triggering).

3.10.3 Resolving Systematic Deformations with theZ0 → µ+µ− Sample

Two systematic cases were covered in detail using theZ0 → µ+µ− sample, the Curl and Twist defor-mations. Both cases gave rise to similar effects in the tracking reconstruction. The Curl deformationeffectively implements a systematic linear shift inφ as a function of the track radius that was indepen-dent of the track pseudo-rapidity,η . In the Twist miss-aligned case thisφ shift also occurs, but as afunction of the trackη , which is shown clearly in 24(b). The Curl case is also clearly picked out by thisgraph, which results in the clear asymmetry signature shownin 25(a), and ultimately results in the shiftin reconstructedZ0 mass, which is shown in 25(b).

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3.10.4 Contribution to the Online Monitoring Program

Several graphs were identified as being particularly sensitive to miss-alignments and are being consideredas test cases for the ATLAS beam commissioning period. Thesegraphs are detailed here :-

• The invariant mass shift using inner detector tracks, whichis plotted versus momentum difference25(b),Z0 η andφ .

• Momentum charge asymmetries plotted against momentum,φ and track rapidities. In addition theraw momentum distibutions are plotted.

• Track matching efficiencies, between the inner detector andmuon system, are plotted for boththe full re-tracking and loose match case ( defined as a track within a ∆R of 0.5 ). Momentumcomparisons between the muon and inner detector systems arealso plotted.

• The impact parameters for the inner detector tracks are plotted against trackφ andη and pT. Thisis done independently for positive and negative charged tracks.

It is believed this sub-set of graphs will be effective in identifying systematic miss-alignments of theinner detector tracking during the commissioning period. The DQMF algorithms will be set-up based onthe initial data-taking experience, and it is expected thatDQMF thresholds will be altered and tightenedas alignment, calibration and hardware operations converge to an acceptable standard for physics qualitydata taking. This will ensure that should changes cause a significant depreciation in data taking qualityit will be quickly flagged and brought to the attention of the shift team on duty.

3.11 Electrons fromW → eνe and Z → e+e− Decays

3.11.1 Introduction

As described in Sec. 2.2, residual weak mode deformations can be present after the full alignment pro-cedure. Some of these weak modes, such as a Curl deformation,will lead to biased track parameters andthus to a wrongly measured trackpT . A powerful quantity to revealpT biases is theE/p for electrons,since the measurement of the energyE in the calorimeter is insensitive to Inner Detector misalignments.To be less sensitive to the calorimeter calibration one can study the difference between theE/p forelectrons and positrons.

ElectronE/p monitoring is included in the Inner Detector performance monitoring through twomodules, theIDPerfMonZee module looking at electrons inZ → ee events and theIDPerfMonWenumodule looking at electrons inW → eν events. The cross section times branching ratio forZ → ee andW → eν processes is not large enough to allow for precise monitoring on a store-by-store basis, thuscollecting data from multiple stores will be necessary. During a store at a luminosity of 1031 we expectapproximately one thousandZ → ee events to be produced.

3.11.2 Electron Selection

An electron used in the electron monitoring modules is selected as a calorimeter cluster matched to anInner Detector track. The calorimeter clusters are required to pass all non-track based cuts included inthe tight electron definition from the egamma group. The track matching algorithm requires a track tobe within 0.1 in∆φ and within 0.05 in∆η . If there are multiple tracks present within this box, the track

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Figure 24: The pT distributions of selected inner detector tracks, split into positive ( red points ) and neg-ative ( blue points ) samples, are shown for various deformation scenarios. A distinct charge dependentbias can be seen for both the deformation scenarios.

with the smallest∆R to the cluster will be chosen. There is noE/p cut imposed on the track matched tothe cluster as this would bias theE/p distribution.

Z → ee events are selected by requiring two electrons, passing therequirements listed above, andwith calorimeter clusterpT > 20 GeV. In addition, the invariant mass of the two calorimeter clusters mustbe between 70 and 110 GeV. There is no cut imposed on the invariant mass of the two tracks.W → eνevents are selected by requiring one electron with calorimeter clusterpT > 25 GeV. The events are alsorequired to have at least 40 GeV of missing transverse energy. The overall efficiency of theZ → eeselection criteria is approximately 40%.

3.11.3 Sensitivity to Misalignments

Figure 26 shows theE/p distribution in simulatedZ → ee events at different levels of alignment. Theshape of theE/p distribution depends on several effects. The high tail is dominated by Bremsstrahlung,which will lead to a lower measured track momentum. The size of this tail is very sensitive to the amountof material in the Inner Detector. LowE/p can either come from the measured track momentum being

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(GeV/c)T

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(a) The charge asymmetry as a function of the inner detectortrack pT for the Curl deformation. This graph shows a clearsignature when there is an overall systematic rotational miss-alignment.

)2 Difference (GeV/cT

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(b) The peak mass of theZ0 resonance also shows a clearshift as a function of the difference between the momenta ofthe two tracks, in the Curl deformation, which is a result ofthe momentum shift shown in graph 24(c).

Figure 25: Monitoring graphs which are currently used to determine alignment quality for high-pT

muons.

too large or the measured calorimeter energy being too low (or a combination of the two). In the case ofsevere misalignments, theE/p distribution gets considerably wider, and the lowE/p region is especiallyaffected. Even for small misalignments, theE/p distribution is somewhat broader, which can be seen bythe increase of electrons withE/p < 1. The ratio between the number of electrons withE/p between0.7 and 1.0 and the number of electrons withE/p between 1.0 and 1.3 is therefore a powerful variableto reveal misalignments. Figure 27 shows this ratio as a function of the calorimeter clusterη . For themisaligned geometry the ratio is very large, due to many electrons havingE/p < 1. The ratio for thealigned geometry is rather consistent with that for the ideal geometry except at smallη , indicating thatone of the two endcaps is not perfectly aligned.

TheE/p distribution itself not only depends on the alignment of theInner Detector, but also on thecalibration of the calorimeter. In addition, it is not straightforward to derive from first principles what theE/p distribution should look like even for a perfectly aligned case. To be less sensitive to these effects,one can instead study the difference between theE/p distributions for electrons and positrons, sincemisalignments will often affect negatively and positivelycharged tracks differently. Figure 28 showsthe difference between the averageE/p for positrons and electrons as a function of the curvature ofthe track. The average of theE/p for positrons and electrons is only derived for electrons with E/pbetween 0.7 and 1.3. With ideal alignment theE/p is not depend on charge and the difference is zero.In both the misaligned and aligned case the difference is different from zero, indicating the presence ofmisalignments.

3.11.4 Selected Monitoring Plots

A set of shifter plots which are sensitive to misalignments have been identified and put into the InnerDetector performance monitoring. These are:

• The efficiency to match an Inner Detector track to the selected calorimeter cluster. Gets comparedto a reference histogram in DQMF.

• TheE/p distribution. The DQMF test requires the averageE/p to fall within a window.

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IdealAlignedMisaligned

Figure 26: TheE/p distribution for electrons in simulatedZ → ee events. The green histogram (opencircles) shows theE/p for ideal alignment, the red curve (open triangles) shows the distribution in asample reconstructed with the full CSC misalignment and theblue curve (open squares) shows theE/pdistribution after running the alignment algorithms on theCSC misaligned geometry. In the presence ofmisalignments, there are more electrons withE/p < 1. The statistics used corresponds to approximately70 pb−1 of Z → ee data.

Cluster Eta-3 -2 -1 0 1 2 3

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Entries 11428

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Entries 11428

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Figure 27: The ratio of the number of electrons withE/p between 0.7 and 1.0 and the number of electronswith E/p between 1.0 and 1.3, as a function of the calorimeter clusterη . The green histogram (opencircles) shows theE/p for ideal alignment, the red curve (open triangles) shows the distribution in asample reconstructed with the full CSC misalignment and theblue curve (open squares) shows theE/pdistribution after running the alignment algorithms on theCSC misaligned geometry. The statistics usedcorresponds to approximately 70 pb−1 of Z → ee data.

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)-11/p (GeV0 0.01 0.02 0.03 0.04 0.05

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Entries 67994Mean 0.01919Mean y 0.002976RMS 0.009371RMS y 1.08










Figure 28: The difference between the averageE/p for positrons and electrons as a function of thecurvature of the track. The averageE/p is only calculated for electrons and positrons withE/p between0.7 and 1.3. The green histogram (open circles) shows theE/p for ideal alignment, the red curve (opentriangles) shows the distribution in a sample reconstructed with the full CSC misalignment and the bluecurve (open squares) shows theE/p distribution after running the alignment algorithms on theCSCmisaligned geometry. The statistics used corresponds to approximately 70 pb−1 of Z → ee data.

• The difference between theE/p distribution for positrons and electrons. A warning or error willbe issued by DQMF if too many bins are significantly differentfrom zero.

• The number of electrons withE/p between 0.7 and 1.0 compared to the number of electrons withE/p between 1.0 and 1.3. In DQMF, the ratio is required to fall within a window.

• The fraction of electrons withE/p between 0.7 and 1.0 as a function of clusterη . Gets comparedto a reference histogram in DQMF.

• The difference, event by event, between theE/p for the electron and positron from theZ bosondecay. In DQMF, the average of the distribution has to be consistent with zero.

All histograms except the last one are filled both for electrons fromZ boson decays and fromW bosondecays.

4 Summary and Conclusion

We have discussed the objectives and the key histograms of the InDetAlignmentMonitoring andInDetPerformanceMonitoring programs. Example histograms from CSC, FDR, weak mode mis-alignments and cosmic ray data were used to illustrate what can go wrong and demonstrate the sensitiv-ity to various problems. The monitoring applications have proven to be invaluable to uncover problemsrelated to the alignment, detector calibration or trackingreconstruction. A subset of histograms willbe used during collision data taking to decide which alignment set to use and if there is a need for arealignment.

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References

[1] The ATLAS Collaboration, G Aad et al, “The ATLAS Experiment at the CERN Large Hadron Col-lider,” 2008 JINST 3 S08003.

[2] J. Alison et al, “Inner Detector Alignment within the ATLAS Full Dress Rehearsal,” ATLAS note tobe submitted.

[3] Alison, J; Cooper, B; Goettfert, T, “Production of Systematically Misaligned Geometries for theATLAS Inner Detector”, ATL-COM-INDET-2009-003, (2009).

[4] J. Alison et al, “Alignment of the Inner Detector using misaligned CSC data,” ATL-COM-INDET-2008-014, (2008).

[5] Corso-Radu, A ; Kolos, S ; Hadavand, H ; Kehoe, R ; Hauschild, M, “Data Quality MonitoringFramework for the ATLAS Experiment at the LHC” ATL-DAQ-CONF-2008-006 ; ATL-COM-DAQ-2007-033.

[6] Degenhardt, J ; Cwetanski, P “Transition Radiation Tracker Data Quality Status Flag Policy,” ATL-COM-INDET-2009-009.

[7] See e.g. M. Venturini and W. Kozanecki, “The Hourglass Effect and the Measurement of the Trans-verse Size of Colliding Beams by Luminosity Scans”, SLAC-PUB-8699 (2000)

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