Topics for the TKR Software Review Tracy Usher, Leon Rochester

Transcription

1 Topics for the TKR Software Review Tracy Usher, Leon Rochester Progress in reconstruction Reconstruction short-term plans Simulation Calibration issues Balloon-specific support Personnel and Schedule TKR Software Review -- Jan. 17,

2 Tracker Recon Where were we at the last workshop? Status at last workshop Pattern recognition & track fit linked together within the context of the Kalman Filter. Tracker reconstruction incorporated within the Centella framework Tracker reconstruction used in the analysis of the Test Beam data and Monte Carlo. Thanks Jose! Plan at that time Separate the track fit from the pattern recognition. Create: Track Extrapolator Pattern Recognition Track Fit (Kalman Fit) Standard HEP Solution Personnel changes Jose departs Tracy and Leon arrive Tracker Recon in the shop TKR Software Review -- Jan. 17,

3 Tracker Recon What is the goal of the reconstruction? To reconstruct the the direction and energy of gamma ray conversions in the tracker. Must find and reconstruct the trajectories of the e + e - pair Must determine the energy of the e + and the e - (individually) Must find the common point of origin of the e + e - pair From this information, determine the energy and trajectory of the incident gamma ray Find and reject background x View 2, Side (Z-Y) TKR Software Review -- Jan. 17,

4 Tracker Recon New kids have a few misconceptions How hard can it be? What happened to the magnetic field? This lead stuff is not helping the tracking! What happened to the stereo layers? Isn t the Monte Carlo supposed to also give you the answer? Electrons don t get along well with others Learning c++ will be easy! Ok, this is not your standard tracking problem Where/how do we get started? y View 3, Plan (X-Z) TKR Software Review -- Jan. 17,

5 Tracker Recon Reality according to Tbsim x x View 2, Side (Z-Y) TKR Software Review -- Jan. 17,

6 Need to: Learn (enough) c++ to be able to understand and modify the code ultimately write new code Learn the GLAST system Learn how the existing code works Get the full appreciation for the problem Make progress along the path set forth at the last workshop Tracker Recon How do the new kids proceed? Approach: 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 TKR Software Review -- Jan. 17,

7 Tracker Recon Pattern Recognition Kalman Filter approach Kalman Filter Particle trajectories are straight lines Changes in trajectory due to multiple scattering are gaussian in nature Pat Rec looks for gammas (vee s), then for particles But Multiple scattering is not entirely a gaussian process Bremsstrahlung results in many low(er) energy e + and e - tracks along principal path Leading to large scattering angles for principle e + or e - And confusing the pattern recon TKR Software Review -- Jan. 17, y

8 Tracker Recon Pattern Recognition simple approach 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 TKR Software Review -- Jan. 17,

9 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 Tracker Recon Pattern Recognition simple approach TKR Software Review -- Jan. 17,

10 Tracker Recon Pattern Recognition simple approach x TKR Software Review -- Jan. 17, y

11 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 Tracker Recon Pattern Recognition simple approach View 4, General TKR Software Review -- Jan. 17,

12 Tracker Recon Some initial results 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 TKR Software Review -- Jan. 17,

13 Tracker Recon Some initial results 3D Accounting Accnt3D Nent = 999 Mean = RMS = Track Accounting ε = 951/1000 = 95.1% # 3D Tracks num3dtrks Nent = Mean = RMS = Number tracks/event <N> = Len 3D trks len3dtrks Nent = 3252 Mean = RMS = 5.58 Length of tracks <L> = TKR Software Review -- Jan. 17,

14 Tracker Recon Some initial results Track Recon Efficiency 3D (>12 Layer 3D tracks) Average Number 3D Tracks Found Per Event Efficiency (%) Fraction 3D found/event Track Energy (GeV) Average Number Tracks/Event # 3D Tracks Track Energy (GeV) TKR Software Review -- Jan. 17,

15 Tracker Recon Some initial results Track Pointing trkangle Nent = 894 Mean = RMS = D Pointing Resolution Take mean value Track Pointing X trkanglex Nent = 894 Mean = RMS = Chi2 / ndf = / 21 Constant = ± Mean = ± Sigma = ± D Pointing Resolution X Fit Gaussian Track Pointing Y trkangley Nent = 894 Mean = RMS = Chi2 / ndf = / 22 Constant = 168 ± Mean = ± Sigma = ± D Pointing Resolution Y Fit Gaussian TKR Software Review -- Jan. 17,

16 Tracker Recon Some initial results Angle resolution (mr) Track Pointing comparison Kalman Fit to First Link Kalman Fit 3D pointing First Link pointing Resolution in 3-D pointing for Kalman Fit and for first link is approximately the same Track Energy (GeV) TKR Software Review -- Jan. 17,

17 Tracker Recon Some initial conclusions 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 New kids are getting to be conversant in c++ New kids have learned a (small) part of the GLAST system New kids have a much greater appreciation of the problem TKR Software Review -- Jan. 17,

18 Tracker Recon Short term plans Balloon flight needs tracking soon! Tested tracking exists within the centella framework Move the existing code The first goal is to move the existing TB_recon into the Gaudi framework Allows us to stop working on legacy code. Connect to New Geometry This will allow us to develop code which can be used for all GLAST configurations. Write new data converters for Test beam data and MC Glastsim output Cosmic data / Balloon flight Continue looking at Pattern Recognition alternatives TKR Software Review -- Jan. 17,

19 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? TKR Software Review -- Jan. 17,

20 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. TKR Software Review -- Jan. 17,

21 TKR Alignment An alignment was done on the BTEM using test beam data, first with entire layers, and then with individual ladders. TKR Software Review -- Jan. 17,

22 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. Residual Fitting planes TKR Software Review -- Jan. 17,

23 The original residuals were as big as 200 µm. (σ 40 µm) Layers and Ladders 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. TKR Software Review -- Jan. 17,

24 Layers and Ladders (2) 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. TKR Software Review -- Jan. 17,

25 Alignment: the New Frontier A 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! TKR Software Review -- Jan. 17,

26 Time-over 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. Distribution of TOT values for a 20 GeV positron run (normal incidence) with a Landau fit overlaid. (Test beam data) t1 t2 TKR Software Review -- Jan. 17,

27 TOT (2) 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 ) γ How do we calibrate the TOT? What is the correct level? e + e - TKR Software Review -- Jan. 17,

28 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 TKR Software Review -- Jan. 17,

29 Charge Sharing, Fluctuations and all that Calculated TOT response is sensitive to details of the generation and digitization. Low Medium High TKR Software Review -- Jan. 17,

30 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 10 7 protons? Same for TOT TKR Software Review -- Jan. 17,

31 A Preliminary Personnel/Task Inventory Tasks Port of Recon to Gaudi Development of Recon Simulation Calibration Institutions Pisa Santa Cruz Santiago de Compostela SLAC People (through mid-feb) 2 TKR Software Review -- Jan. 17,

32 A Preliminary Personnel/Task Inventory And tentative set of matches... Tasks Port of Recon to Gaudi Development of Recon Simulation Calibration Institutions Pisa Santa Cruz Santiago de Compostela SLAC People (through Mid-Feb) 2 TKR Software Review -- Jan. 17,

33 TKR Software Schedule Near-term (Feb Mar) Port recon to Gaudi Read TB data/mc -- verify port Refine recon algorithms Implement digitization Read/recon cosmic data Specify calibration databases Medium-term (Apr May) Refine digitization Read/recon Glastsim/GEANT4 digis Implement calibration databases Implement single-tower alignment algorithm Test alignment code with cosmics Implement hot/dead strip calibration Write special code for balloon flight Refine TKR-specific GEANT4 code Long-term (June ) Refine balloon recon Perform balloon analysis Connect new geometry Implement full detector geometry Develop multi-tower alignment algorithm Demonstrate full detector capability gamma detection and measurement background rejection optimized PSF automated calibration TKR Software Review -- Jan. 17,

34 TKR Software Review -- Jan. 17,