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Summary of the Emulsion Reconstruction WG

Summary of the Emulsion Reconstruction WG. P. Migliozzi.

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Summary of the Emulsion Reconstruction WG

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  1. Summary of the Emulsion Reconstruction WG P. Migliozzi S. Aoki, L. Arrabito, A. Badertscher, M. Besnier, C. Bozza, E. Carrara, M. Cozzi, G. De Lellis, M. De Serio, F. Di Capua, L. S. Esposito, T. Fukuda, M. Guler, F. Juget, K. Kodama, M. Komatsu, J. Knuesel, I. Kreslo, I. Laktineh, A. Longhin, G. Lutter, K. Mannai, A. Marotta, F. Meisel, P. Migliozzi, A. Pastore, L. Patrizii, C. Pistillo, L. Scotto Lavina, G. Sirri, T. Strauss, V. Tioukov, A. Zghiche

  2. List of activities • Tracking in an ECC (M. Besnier, C. Bozza, T. Fukuda, K. Kodama, I. Kreslo, Y. Nonoyama, C. Pistillo, C. Sirignano, V. Tioukov, Zghiche) • Vertex location as a function of the event classification (L. Arrabito, C. Bozza, M. De Serio, I. Kreslo, A. Marotta, Y. Nonoyama, C. Pistillo, C. Sirignano) • Volume scan, vertex reconstruction and decay selection based on topological criteria (M. Besnier, C. Bozza, F. Di Capua, T. Fukuda, K. Kodama, M. Komatsu, I. Kreslo, A. Marotta, Y. Nonoyama, A. Pastore, C. Pistillo, L. Scotto Lavina, C. Sirignano, V. Tioukov, A. Zghiche) • Brick to brick connection (E. Carrara, M. Komatsu, A. Longhin); • Momentum measurement by MCS criteria (M. Besnier, C. Bozza, M. Komatsu, C. Sirignano, A. Zghiche) • e/pi separation and energy measurement (S. Aoki, F. Juget, F. Meisel); • p/pi and pi/mu separation (S. Aoki, T. Fukuda, I. Kreslo, I. Laktineh, K. Mannai, C. Pistillo); • Post-scanning 1mu/0mu classification (Y. Nonoyama); • Kinematical decay selection criteria (C. Bozza, A. Marotta, Y. Nonoyama, C. Sirignano)

  3. Now available on the wiki page)

  4. Trigger Electronic detectors Brick finding Vertex location Emulsions Decay search “long” or “short” decays m / e at1ryvtx ? yes Classify as nm / e Emulsions Electronic detectors no tdecaymode Kinematics ntevents

  5. Vertex location

  6. Preliminary results for the the t->m DIS and t->m QE channels t->m DIS LONG • etrigger in emulsione = 95% • econferma del trigger = 99% • escanback = 96% • eidentificazione topologia = 94% • eidLONG = 92% • eidSHORT-LIKE = 8% SHORT • etrigger in emulsione = 93% • econferma del trigger = 98% • escanback = 91% • eidentificazione topologia = 98% t->m QE LONG • etrigger in emulsione = 84% • econferma del trigger = 99.9% • escanback = 97.7% • eidentificazione topologia = 95.6% • eidLONG = 83% • eidSHORT-LIKE = 17% SHORT • etrigger in emulsione = 82% • econferma del trigger = 99% • escanback = 62% • eidentificazione topologia = 98.9%

  7. + D / t / / 0 0 D p p p e + / e + p g A proposal for 0m primary vertex location Cristiano BozzaSalerno Emulsion GroupPhys. Coord. May 2007

  8. / 0 p p Types of NC-like events g h t production, decay to h 1) t- X e- 2) t production, decay to e(shower likely) t- X NC showerless nm 3) h h nm g NC with shower 4) e- g h

  9. / 0 p p Main problems of 0-m g 1) TT prediction cannot define a precise slope/position pair  reduced filtering function of CS 2) Many tracks can be found in CS (mostly type 2 and 4) scanback takes time with many paths 3) Scanback paths are likely to lead to 2ry vertices; sizable probabilityof not finding 1ry vertex by direct scanback nm g However, what we can do witha brick is Scanback + Volume ScanSolution should be found there e- g h h stop in brick

  10. / 0 p p Further considerations g Scanning load cannot be increased too much Lead ECC is a relatively dense material – EM 2ry interactions should be near to the 1ry (X0=0.56 cm  4 cells, 9/7X0=0.72 cm  6 cells) Track multiplicity is very high in showers, but low momentum e+/e-are strongly scattered and travel a short length Scanback is efficient in finding interaction points quickly

  11. / 0 p p Strategy – Step 1 g CS Scanning Search for base-track pairs on CS (no 3-out-of-4) DSCS = slope differencebetween base tracksDSCS < 0.015 Rank tracks with DSCS and select the first NpCS = 300 Goal of step 1: discard low momentum tracks as soon as possible

  12. / 0 p p Strategy – Step 2 g CS – Target connection Project CS pairs to first two plates of target using most upstream pos/slope ACST = area to be searched500500mm2 DSCST = 0.040 (CS-brickmisalignments possible) Pick up all candidates for each track Goal of step 2: minimize track losses (scattering should be small)

  13. / 0 p p Strategy – Step 3 g Scanback Follow scanback paths with the same parameters as for m in CC Many scanback paths with low momentumare lost very soon (hard SB parameters) DSSB = 0.020 DPSB = 80 mm Max missing plates NmpSB = 5 plates Goal of step 3: follow tracks with high momentum as upstreamas possible, and discard low momentum tracks quickly

  14. / 0 p p Strategy – Step 4 g TotalScan/NetScan Choose the NV = 10 paths stopped most upstream (except passing-through paths) TotalScan around most upstreamstopping pointsUse latest direction to search for 1ry vertex – skewed volumesVolume width grows upstream (AS = slope acceptance  0.4) Correlation between 1ry vertexposition and products of 2ry interactions Nd Nu Catch g conversions and charm decays: Nu = 10 Goal of step 4: limit complexity of scanning procedure despite of a small increase in scanning load

  15. / 0 p p Scanning load and data size g Step 1: 240 cm2×2 sides = 480 cm2 Scanning time for both CS = 24 h (at 20 cm2/h/side) (lower if prediction scan is used)Data size = 60 MB for 105 tracks in each CS Step 2: 0.75 cm2×2 sides = 1.5 cm2 Scanning time = 4min30s (at 20 cm2/h/side) Data size = negligible Step 3: 300 predictions×57 plates Scanning time = 5h42min (at 1.2s/track) Data size < 5 GB Step 4: 115 cm2×2 sides Scanning time = 11h30min (at 20 cm2/h/side) Data size < 5 GB Total: 41h/brick, < 10 GB/brick

  16. / 0 p p Conclusions g The procedure should be able to fulfill several conflicting goals Efficiency should be estimated If 1ry vertex is not found, event interpretation is affected estimate resulting background Scanning load and data size acceptable Many parameters can be optimized  work for MC experts!

  17. Comments • The vertex location for events with a muon in the final state works very well (despite of the low base track efficiency, the usage of micro-tracks helps) • The situation for 0mu-like events is more difficult. More efforts are needed if we want to be ready by September • We should review this item by mid of July

  18. Vertex Reconstruction L. Arrabito, M. Besnier, C. Bozza, A. Pastore, L. Scotto Lavina, V. Tioukov

  19. Summary Goal : - Analysis of vertex reconstructionofnm CC neutrino interactions Data set: - Monte Carlo simulation of3000nm CC events generated by OpRoot-ORFEOv7 Properties: - Monte Carlo data (TreeMSE) withsmearing and efficiency correction ( eff = 0.944 – 0.216 * q – 0.767 * q2 +1.856 * q3 ) Analysis: - Tracking and Vertexing performed by Fedra (Similar results have been obtained by using the AlfaOmega framework)

  20. energy spectrum of interacting nm X (cm) Interactions inside the OPERA brick Y ( cm ) Z (cm) Analysed data sample - 3000nm CC events - CNGS energy spectrum

  21. m nm nm CC interaction

  22. -5 -4 -3 -2 -1 P0 +1 +2 +3 +4 +5 Pb plate (X0,Y0) Emuls. film Fiducial volume Neutrino interaction vertex is at the center of the fiducial volume muon Volume size : 25 mm2 * 11 plates P0 = first emulsion sheet containing the neutrino-associated m ( X0, Y0 ) =m position at ZP0

  23. MC truth vs MC reconstructed vertices MC truth primary vertex n secondary tracks primary tracks reconstructed primary vertex MC rec secondary track wrongly attached to the vertex reconstructed primary tracks

  24. sx = 0.34 mm sy = 0.37 mm sz = 2.77 mm MC truth vs MC reconstructed vertices: vertex position

  25. 1) Study of nmCC Nf<1% sxy= 1.1µm sz=8.9µm

  26. MC truth p+ p p- e+e- MC rec p+ p- p e+e- MC truth vs MC reconstructed vertices: interaction products

  27. secondary tracks wrongly attached to the neutrino vertex hadrons e+,e- dz < 1300 mm  23 % of “wrong” tracks survive tracks really belonging to the neutrino vertex hadrons e+,e- dz < 1300 mm  97 % signal selected MC truth vs MC reconstructed vertices: interaction products

  28. Vertex detection efficiency Purity (all tracks attached to the vertex are primary)> 99 %

  29. 50% of generated tracks with P<1GeV  large angles Low reconstructed multiplicity Vertex detection efficiency: dependence on momentum e(tracking) ~ 100% for P>1GeV, drastically decreases below 1 GeV e(vertexing) ~ 95% for P>2GeV, drastically decreases below 1 GeV

  30. Summary of present activities on vertex reconstruction and decay search Overall summary (1/4) The data/MC comparison on 8 GeV pions shows that data behavior is compatible with MC expectations, apart IP distribution, where a strong discrepancy is present at small values. The IP distribution discrepancy must be understood. The plate misalignment, together with the inefficiency and the track smearing already simulated, could (at least partially) explain it. Investigations are in progress. • So far, the data/MC comparison on pions is used to make a systematic comparison between data and our MC. • To predict the exact IP distribution found in data we need to simulate all the effects: • tracking inefficiency; • track parameters smearing; • plate misalignment; • cosmics and uncorrelated background.

  31. Summary of present activities on vertex reconstruction and decay search Overall summary (2/4) Studies on  CC interactions show that: the tracking efficiency is ~100% for P> 1GeV, drastically decreases below 1 GeV; the vertexing efficiency is ~95% for P > 2GeV, drastically decreases below 1 GeV Studies on  CC () and  CC (3h)events show that: Several selection categories are populated by events with low momentum particles, in particular the momentum of particles from  decay All these simulations don’t take into account the effects of electronic reconstruction and neutrino location on the neutrino energy spectrum. Concerning  events, they are roughly using the CNGS spectrum without taking into account the energy dependence of oscillations. The neutrino oscillation effect is very easy to reproduce. The electronic reconstruction and neutrino location effects have been parametrized in function of neutrino energy

  32. Summary of present activities on vertex reconstruction and decay search Neutrino energy spectrum CNGS interacting CNGS with m2=2.5x10-3 Interacting CNGS after electronic reconstruction

  33. Summary of present activities on vertex reconstruction and decay search Overall summary (3/4) Studies on multiple vertices events like  CC (3h)and charmed events show that low efficiencies and purities occur in the reconstruction and correct recognition of vertices while reconstructing 2 vertex in the same fiducial volume. The reason is the confusion between track associations when the primary and the secondary vertex are too near each other. Such effect is amplified where tracks have low momentum and high angles and by the presence of fake vertices (wrong associations, interactions, e-pairs,...). A study for the e-pair rejection is shown. Pair Based Vertexing algorithms implemented in FEDRA cannot be used for the topologies recognition as they are. The pairs association should be studied according to the analysis peculiarities. Global Vertexing method could be more effective and its effectiveness is under study. The study of the microtracks near the vertices could play an important role.

  34. Comments • The vertex reconstruction is well under control for νμ events • There are different algorithms with similar performance. We are in the process to select the “best” algorithm • The decay search algorithms have to be tuned. In particular, it was shown that • The hunting for short decays (decays in lead) has to be optimized • The search for multi-prong decays is more difficult than single-prong. An approach on the so called “Global vertexing” is being tried • The usage of micro-tracks is mandatory

  35. Momentum measurement by MCS M. Besnier

  36. MC/data study Data fome from TBàCERN in 2002-04 Perfect MC linearity, shift for 4 GeV data (250MeV offset) Resolution update after fit range studies The MC indicates that it is possible to measure momentum until 8GeV with a resolution of 26%.  independently determined!

  37. Large angle results 3 effects appear at large angles ( >0.1rad ) 1) Crossed Lead thickness more important MC 4GeV pions at different 3D angle : 0.1 0.2 0.3 0.4 0.5 0.6

  38. How to determine dq correctly with OPERA track configuration ? -First idea of using passing-through cosmic tracks to evaluate the dq has to be reconsidered because of wide angular and momentum dispersions. qx (rad) for cosmic muons Pgen (GeV) for cosmic muons with qx/y < 0.4rad Alberto’s cosmics simulation -PMS at 0rad is now implemented in FEDRA with a dqs set to 1.8mrad. The Z correction with slope is also taken into account. But no dq dependance with slope => wrong momentum estimation at large angles. -A way to get the dq is to parameterise its value with data and update it often.

  39. Conclusion : • Some updates on momentum resolutions at 0rad : fitting range does not exceed 14 plates. • dq free in the calculation is a wrong way to evaluate the momentum • dq angular dependance (under studies) : • -should be parametrised in X and Y directions separately • -or should be avoided by changing coordinates frame A draft discussing the results related to the first 2 points is in preparation

  40. OPERAAnalysis status in Neuchatel Frank Meisel, Frederic Juget, Guillaume Lutter 01.06.2007 Université de Neuchâtel

  41. Status in Neuchatel • Three major projects on emulsion reconstruction (besides scanning): • Developing and integration of an advanced shower reconstruction algorithm/library into FEDRA • Testing different methods of shower reconstruction • Continue/Improve the energy measurement of an electromagnetic shower

  42. libShower • First version has been adopted by Frederic, can be used • still options for improving / modifing • gives reconstructed shower output file • user has to decide which showercandidates to put in... • In the near future me (FWM) will provide idea/implementation of a shower reco using maybe tracks or more sophisticated • (up to know we have to know the initiating basetrack)

  43. Testing different methods shower reconstruction • Using different parametersets to find best set for efficency / purity of a shower with a induced (microscope) bg. • slightly modified algorithm (going downstream instead of upstream) • ConeTube, Neuchatel scanned empty (peanut) Brick for BG • have to scan over 250Mio. parametersets->taking long time.... still running......(Submit on any cluster machine prefered) • taken then best paramters in ShowerReco and for energymeasurement. For example: Electron energy and BG contamination

  44. Continue/Improve the energy measurement of an electromagnetic shower • e/pi_Algorithm and variables for e/pi separation taken over: • Number of Basetracks, dR, dTHeta distributions (mean, rms) • Longitutinal profile (number of BT per each plate (11...56 sheets) • ANN Structure • InputNeurons: 5+#LongProfile => 16...61 variables • HiddenLayer1,2: n+1, n (n=InputNeuron) • OutputNeuron, 50 TrainingsEpochs on the cont. sample (0.5..6GeV, 0.5..10GeV) 35kEvents • Energy correction has to be done on the output • Linear fit function: E_(measured) -> E_(true) • Run again with: E_(measured) -> E_(corrected) • Plots/Results for the first ParameterSet:

  45. ANN Outputs • Before Linear Energy Correction (Trained on 0.5..10GeV, 20Sheets) • After Linear Energy Correction

  46. Before Linear Energy Correction After Linear Energy Correction 20 sheets Shower Resolution can be improved (now ...~50%/Sqrt(E))

  47. Summary • Neuchatel is continuing on scanning and simulation: • energy measurement • slight improvements in energy resolution: but to early to have complete datasets • insert into shower package also... • Shower Reconstruction (focused on e for now) • slight improvements in efficency, purity: but to early to have complete datasets • developing convenient and useful shower algos and their insertion in fedra • still ongoing....

  48. m/pseparation

  49. Results of Perrine Royole (IPNL) dE/dx (data) Using only the dE/dX

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