1 / 26

Minimum Bias Low Pt Tracking

This overview discusses the Low Pt tracking methods used in minimum bias events at the LHC, highlighting the significance of accurate tracking for understanding the dN/dη predictions. A detailed summary includes motivation, objectives, and methodologies employed in the study, such as the use of the MBTS trigger and issues encountered during data selection and reconstruction. Key findings emphasize the necessity for improved tracking conditions to achieve better efficiency and purity in the results, providing insights for future LHC experimental strategies.

shamus
Télécharger la présentation

Minimum Bias Low Pt Tracking

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Minimum Bias Low Pt Tracking W. H. Bell 08/02/2007

  2. Overview • Motivation • Recap • Low Pt Tracking • Objectives • Steering reconstruction • Track Quality Cuts • MBTS Trigger Efficiency • Conclusions and Outlook

  3. Motivation • Predictions of dN/dη at LHC uncertain • Compare NSD with previous results • Use minimum bias measurement as a Monte Carlo tuning input and for pile-up studies.

  4. LHC Predictions σmb approximately 65mb Integrated luminosity of 10pb-1 => 1012 Events

  5. Minimum Bias Trigger Scintillators 2 segments in η 8 segments in φ 2.0 < η < 3.8 inner η outer η

  6. Recap ε From Generator Truth, requiring a Lvl1 MBTS trigger ε Inelastic non-diffractive Inelastic Double and Single Diffractive

  7. Low Pt Tracking • Tracker is in principle sensitive to soft tracks • Pt = 400 MeV - tracks reach end of TRT • Pt = 150 MeV - tracks reach last SCT layer • Pt = 50 MeV - tracks reach all Pixel layers 50MeV 150MeV 400MeV A.Salzburger - Fatras simulation

  8. Low Pt Tracking: Steering Reconstruction • InDetRecExample provides examples • Implement via modified RecExCommon_topOptions.py • minPT = 75MeV • GlobalChi2Fitter • maxHoles = 1

  9. Track Quality Cuts • |d0| < 50mm • |z0| < 1000mm • Pt > 75MeV • No. Silicon Hits >= 4 (of 11) • No. Pixel Hits >=1 (of 3) • No. of holes <=1 • Χ2/d.o.f <= 6.0 • Primary track: require |d0| < 10mm

  10. Data Selection • Pythia 6.4 @ 14TeV • 50k Inelastic non-diffractive • 50k Inelastic single and double diffractive • Data passed through ATLAS Geant simulation and digitization • Generation of MBTS LVL1 containers • Reconstruction run with low pt tracking settings

  11. Problems • All inelastic non-diffractive jobs crashed • 44% of inelastic diffractive jobs crashed • Known problem within tracking software

  12. Reconstructed Tracks: Pt distribution

  13. Reconstructed Tracks: η distribution

  14. Track Quality Cuts: Pt

  15. Track Quality Cuts: η

  16. Track Quality Cuts: Pt vs χ2/d.o.f

  17. Track Quality Cuts: Pt vs Silicon hits

  18. MBTS Trigger Efficiency: Pt

  19. MBTS Trigger Efficiency: η

  20. Conclusions and Outlook • MBTS trigger requirement has no η bias when looking at low pt reconstructed tracks from inelastic diffractive samples. • Better track reconstruction exit condition needed • Need efficiency and purity plots • Affect of Ks? • Vertex constraint?

  21. Backup

More Related