1. 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
UW Analysis Meeting Summer 2014

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

3. Part 1 (EUTelescope Testbeam Analysis )

4. 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)

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

6. 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

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

8. 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

9. 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

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

11. 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

12. Results

13. Hitmaps
Batch 0b
Batch1
Batch2a
Batch3a

14. Results for Batch 0b
Efficiency Projection Y

15. Results for Batch 1
Efficiency Projection Y

16. Result For Batch 2a
Efficiency Projection Y

17. Result for Batch 3a
Efficiency Projection Y

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

19. 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.

20. 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

21. Part 2 (Inner Detector Alignment)

22. 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.

23. 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.

24. Example of Weak Modes
From :

25. 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.

26. 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.

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

28. 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

29. 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.

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