Tracker Software

Tracker (TKR) information will be used to precisely identify the impact point and the path length of the ions in the plastic scintillators of the ACD and the CsI logs of the CAL.

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A special run with a Xe beam made available by GSI was performed. The number of TKR clusters becomes very high and the cluster size increases up to 15 strips. A dedicated analysis to identify secondary fragments at lower Z is underway.

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• Take on the task of separating the pattern recognition from the track fit
• Work within the Test Beam / Centella framework
• Implement a new pattern recognition (“link and tree”) which is independent of the Kalman Filter
• Attempt to perform a simple pointing resolution study comparing fit tracks to the first links

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• Implement a “Link and Tree” algorithm
  • Simplest algorithm to dive into the code
  • Allows one to follow pre-shower development
  • “Longest, Straightest” branch is trajectory of the primary e+ or e-
• Can be projected to calorimeter for initial clustering
• Passed to Kalman Filter for track fit
  • Other branches can (hopefully) give more information on energy of primary e+ or e- pair
  • As with Kalman Filter, Pattern Recognition runs in 2-D
• Association to 3-D done after initial 2-D tracking finding
  • Strategy is to find individual tracks first
• Then put tracks together to form/find gamma conversions

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• Algorithm
  • Links formed between all pairs of clusters in adjacent layers
  • Beginning with the top most layer containing cluster hits, links are combined to form a tree structure
  • Links are not allowed to be shared
  • Clusters are allowed to be shared
  • Trees sorted by longest and straightest for association to 3-D

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• Current Status
  • “Link and Tree” algorithm in 2-D operational
  • Rudimentary association of 2D tracks to 3D operational
• Tracks start in same layer
• Tracks have same length
• Straightest tracks associated
  • Longest, straightest tracks are fit by Kalman Filter
  • Only looking at single charged particles at this point
• Not quite ready for gammas

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• Look at TBsim e+ runs
  • Positrons incident normal to the first tracker layer
  • Energies: 0.1, 0.25, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0 GeV
• 3-D Track Reconstruction of e+ requirements:
  • Track must be 12 or more layers in length
  • Must start in first tracker layer
• Look at:
  • Track recon parameters
• Number reconstructed and passing above cuts
• Length of tracks
• Etc.
  • Pointing at start of track comparing
• Compare between fit parameters at first hit and first link

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• 3-D Pointing Resolution
  • Take mean value
• 2-D Pointing Resolution X
  • Fit Gaussian
• 2-D Pointing Resolution Y
  • Fit Gaussian

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• “Link and Tree” Pattern Recognition
  • Simple algorithm implemented within the Centella context
  • Shows promise for:
• Finding primary e+ and e- tracks
• Keeping track of pre-shower development – aid in helping to keep track of energy loss of the primary tracks
• Providing initial pointing into the calorimeter
  • Don’t need to know the energy before getting the track
  • Track finding – calorimeter – track fit – calorimetry – track fit - …
  • More careful studies needed before really saying anything about pointing resolution…
  • Needs refinement (= rewrite) if really want to proceed…

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Calibration Issues

There are three parts to each problem below: the calibration algorithm, the database, and the automated production process

• Bad Strips
  • Hot/dead strips
  • Common mode failures: Chips, ladders, towers
• Alignment
  • Current status
  • What’s ultimately needed
• TOT (Time-over-Threshold)
  • Calibration signal?

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Bad Strips

• Currently, the bad strips are recorded in an ASCII file, by layer and strip number. These are used by the reconstruction to kill bad strips and to join clusters separated only by bad strips.
• For the full detector:
  • The production database will record bad strips at different levels: for example, chips, detectors, ladders, layers, and (shudder!) towers.
  • Since the state of the strips will need to be monitored regularly, we particularly need a reliable automatic system to detect bad elements and update the database.

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TKR Alignment

An alignment was done on the BTEM using test beam data, first with entire layers, and then with individual ladders.

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Finding Residuals

Method: Find the a track.  Fix a line through the clusters in planes 8 & 15.  Calculate the residuals with respect to that line.

The original residuals were as big as 200 μm. (σ » 40 μm)

Layers were shifted to minimize the residuals.

Two layers can be fixed (or the overall change of position and slope can be set to zero) because there are two degrees of freedom in the original problem.

The resulting residual distribution has σ » 25 μm.

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Layers and Ladders

To improve resolution, the positions of individual ladders were adjusted with respect to ladders above and below, using normally-incident tracks, with
the final σ » 15 μm.

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Alignment Complication

In the full detector, many tracks will cross ladders and towers.

Slanted tracks allow the alignment of adjacent ladders and towers.  This is more complicated because now all the elements are tied together with springs, and there are six parameters per object:

 x, y, z, and 3 rotations.

Solving this problem usually leads to big matrices!

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Time-over-threshold (TOT)

For each layer, the TOT is measured by combining all the fast-OR’s for each event.

The TOT measures the width of the pulse at some fixed pulse height, and is thus roughly proportional to the largest charge deposited on any strip in the layer.

In the test beam, the TOT was sensitive to the photon conversion point.  But this was at normal incidence.  Will this still work for angled tracks?  We have test beam data to answer this question! (I think…)

• How do we calibrate the TOT?
• What is the correct level?

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Plans for Simulation

• Glastsim/GEANT4 outputs MC truth
• Digis produced from MC hits
  • Digis and hits can be read by Recon
• Upgrades to Generation
  • Realistic Geometry
  • Fluctuations (for TOT)
• Upgrades to Digitization
  • Charge sharing
  • Dead strips/chips/SSDs
  • Overlay of background
• Model
• Real data
  • TOT
• Common Geometry

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Charge Sharing, Fluctuations and all that ...

Calculated TOT response is sensitive to details of the generation and digitization.

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Balloon-specific Support

Certain aspects of the balloon-flight data may require special support.

• special reconstruction algorithms
  • Dealing with high backgrounds
  • Picking out photons in hadronic showers
• Analysis
  • Projecting to active targets
  • Finding interaction vertex
• Calibration
  • Dead/hot strip list
  • Probably no alignment required to reconstruct tracks, but we may want to demonstrate that we can do it. A use for the expected 107 protons?
  • Same for TOT

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Tasks:

• Port of Recon to Gaudi
• Development of Recon
• Simulation
• Calibration

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