Tun Sheng Tan AUGUST 25 2014 UW Analysis Meeting Summer 2014 EUTelescope Testbeam Analysis: Summer 2014 / Inner Detector Alignment Final Report Tun Sheng Tan AUGUST 25 2014 UW Analysis Meeting Summer 2014

Content Part 1 (EUTelescope Testbeam Analysis ) Project Background AFP Detector Objective Issues Result Conclusion Part 2 (Inner Detector Alignment) Future plans

Part 1 (EUTelescope Testbeam Analysis )

Project Background Atlas Forward Physics detector(AFP) Identify diffraction of protons from the point of interaction Using 3D sensor Require efficiency of 99.9% even at the edge Slim edge facing beam About 200m from point of interaction Must be able to maintain high efficiency in highly non-uniform radiation R. Staszewskiy, “The AFP Project” (2001)

Slimmed edge sensors To reduce amount of dead area on sensor To minimize the need to overlap sensor and reduce multiple scattering

Objective To plot the efficiency map of the sensor To plot the S-curve for the edge efficiency To run the analysis over a batch of data

EUDET telescope *** No cooling box used in my set up

Overview of Track Reconstruction EUTelescope is a software that converts RAW data output and a ROOT file with the fitted tracks Converter From raw data to LCIO format (Linear Collider In/Out) Information from a single trigger such as Column, Row, Time Over Threshold, Level 1 Trigger and Readout ID into a single event. Clustering Group pixel hits to a cluster Information of cluster is not store in the ROOT file Hit maker Identify the hit position from the cluster Alignment Determine the alignment parameters Fitter Reconstruct the track using the data from hit maker and alignment Produce ROOT file

Batch Configuration Batch 0b 1 2a 3a Module ID AFP_CNM_S3_R5 AFP_FBK_S5_R10 AFP_CNM_S5_R7 AFP_FBK_S1_R9 Front-end FE-I4B Fluence unirrad For FBK the slim edge consists of a multiple ohmic column fence, that prevents the depletion region spreading from the outermost junction column from reaching the cut line For CNM the slim edge consist of a guard ring hence using 3D electrodes, able to sink the leakage current originating from the cut line, and surrounded by a fence of ohmic columns. FBK CNM

Reconstruction Configuration Using EUTelscope v00-08-02 to convert raw data to root file. Using Jörn gear files (June 2013). There is a bug in v00-08-02. Row 0 is excluded(Jörn ). Required bug fix: Change Eutelescope/v00-08-02/src/EUTelAPIXClusteringProcessor.cc (l. 554) from (x > 0 && x < 10000 && y > 0 && y < 10000) to (x >= 0 && x < 10000 && y >= 0 && y < 10000)

Issues Inconsistency between projection efficiency graph and efficiency map Joern pointed out the problem with conversion formula (from physical units to column-row units coordinate system). The shifting of origin in efficiency map is not needed. Setting up EUTelescope on SL6 With help of Todd, required packages are installed on TeV clusters. Latest EUTelescope (v9.3) does not work No solution

Results

Hitmaps Batch 0b Batch1 Batch2a Batch3a

Results for Batch 0b Efficiency Projection Y

Results for Batch 1 Efficiency Projection Y

Result For Batch 2a Efficiency Projection Y

Result for Batch 3a Efficiency Projection Y

Compare to the sample: Summary: Results are consistent with sample. My Result Sample My Result Efficiency Projection Y Summary: Results are consistent with sample.

Batch 1 (FBK) Batch 2a (CNM) Batch 3a (FBK) Summary: FBK sensor has a longer range of sensitivity beyond row zero. Batch 1 and 3a results are consistent.

Conclusion Successfully reproduced sample batch 2a result and for the rest of the batches The batches meet sufficient edge efficiency requirement Confirm the differences between CNM and FBK sensor

Part 2 (Inner Detector Alignment)

Project Background Detector is made up of more that one component. When fitted together each parts will not always stays at the exact position after construction. Intrinsic resolution is better than assembly precision and mechanical stability. Alignment is crucial for identifying vertex and jet tagging.

Weak mode Detector alignment is needed in order to achieve the tracking performance required by the physics objectives. When performing alignment, weak modes are a major concern. They are eigenvectors with small or vanishing eigenvalues. The weak modes that correspond to detector deformations are problematic because they effect physics results. The easiest and most effective method for eliminating potential weak modes is by combining tracks from events with different topologies. Different types of events will lead to different types of weak modes. In particular, cosmic-ray muons provide a source of events with a wide range of track topologies different from those in collision events.

Example of Weak Modes From : http://atlas-service-enews.web.cern.ch/atlas-service-enews/2007-8/images_07-8/alignment3_511.jpg

Alignment Validation After alignment process, one needs to do validation of the alignment. Chi square is should be check to ensure minimum. Basic track quantities, number of tracks, number of hits on track, should increase. The next check is residual distribution. The residual is defined as the distance between the local input measurements and fitted track position. The residual distribution is the plot of the residual summed over many hits on track. Residual distribution should be a Gaussian centered at zero with a good alignment. However, this validation process is not perfect. The alignment can converge and the residuals improve despite the fact that weak mode detector deformations are present.

Known physical resonances can also serve as standard candles against which weak modes can be probed. For example, the invariant mass and width, of the K0, S, J/ψ, Υ, and Z can be used. Or cosmic-rays muon track can also be used to validate. The muons can traverse the entire ID barrel and two tracks(upper and lower halfs) can be constructed from a single physical object. Thus, both tracks should have the same parameter.

Objective To complete tutorial on running the alignment software To be able to do basic monitoring To test different pixel clustering strategy

Progress Completed tutorial on basic alignment jobs: Simple run Iteration run Performed 3 iterations of alignment process for multi-muons Batch queue Performed alignment process in parts then merge to combine results

Issues When trying to run monitoring script: $ python MakeAlignmentMonitoringPlots.py -c [configurationFile] Error: EXITING because failed to find histogram /IDAlignMon/ExtendedTracks_NoTriggerSelection/Residuals/pix_b_residualx_fine Missing ExtendedTracks directory.  I do not understand the complete workflow the alignment procedure. Requires more reading on Twiki page.

Future plans Run monitoring script on the tutorial sample Test different different pixel clustering strategy