Standardized Summary Plot for RPC Analysis: Enhancing Data Interpretation and Comparison

1. RPC DQM C. Carillo, A. Cimmino, D. Lomidze, M. Maggi, P. Paolucci

2. Outline • New set of summary plot • Standardization of summary plots (type and colors) • Plot Normalization (to compare them) • Algorithm to summarize the roll status - under study • New algorithm for the Run Summary - under study

3. Standard Summary plot • We would like to propose to adopt a standard summary plot for the RPC, based on the following features: • Roll versus Sector (204 entries x wheel) • 5 plots per parameter for the Barrel • Colored map (fixed color code) • Normalized (see next slide) • Fixed Y scale

4. Summary Plot normalization • We are working on the normalization of all the summary plots in order to have an immediate and easy way to compare and interpret the results: • Compare the results of different chambers in the same run  geometry • Compare the results of the same chamber in different runs  # of RPC event or # entries • Normalization give us the possibility to have a fixed Y scale and fixed color map • This helps a lot the person in shift

5. New set of Summary Plot (I) Roll .vs. Sector • Normalized Occupancy(norm to geom and event) •  high occupancy = red = very noisy roll •  zero occupancy /white = chamber/electronic OFF • Mean Cluster Size • # of entries in the first bin of cluster size (norm entries) •  if 0.95 < FB < 1 = few strips very noisy • # event with cluster size > 5(norm event) •  many events = red = very noisy roll • % of dead strips • Occupancy Asymmetry (shape studies) Total 30 summary plots for the Barrel; still few redundancies

6. New set of Summary Plot (II) • Distribution or 1D histo (per run) very useful to study our cut (alarm/warning) and to check if there are overall effects in some run/period • Normalized Occupancy by sector (geom and event) • RMS occupancy by sector • Mean Cluster Size • # of entries first bin of cluster size (norm event) • # event with cluster size > 5 (norm event) • Occupancy Asymmetry (shape studies) • Occupancy roll versus strips (old)

7. Normalized Occupancy Very uniform wheel (CRAFT run 70674) High Occupancy Off or dead chamber

8. Mean Cluster Size distribution Calculated by the Cluster size per roll (for every run) <CS> = 1.7

9. First-bin cluster size • # of entries in the first bin of the cluster size distribution (normalized by # of entries) FB entries / total entries This variable has been thought to spot roll with small set of non adjacent noisy strips 3 rolls

10. First Bin Cluster size - roll .vs. sector Noisy Strips ? FB = 0.99 & High Occ Noisy strips ? Chamber OFF

11. Occupancy by strip FB = 0.99 && High Occ  Noisy strips ? ---------------------------- YES 5 noisy strips

12. Occupancy by strip FB = 1 && High Occ  Noisy strips ? ---------------------------- YES 2 noisy strips But dead chamber

13. Occupancy – roll versus strip 2 Noisy Strips Chamber OFF ? A second way to spot the same problem

14. Dead Channel percentage – roll .vs .sector Dead channel are counted by the occupancy distribution by strips Third way to spot the same problem

15. Mean Cluster size CS = 3 ? Chamber OFF

16. # events Cluster size > 5 - Wheel 0 Many events With CS > 5 Noisy Chamber ? NO

17. How spot this kind of problem ? Bad shape of the roll occupancy distribution by strips • Bad shape corresponds to: • large holes • asymmetries • missing region/FEB/Chip

18. Mean & Left-Right Asymmetry cut ALR = (# left - # right)/(# left + # right) cut Amean = abs (mean - # strip/2) baricentrum

19. Left-Right Asymmetry Not able to spot it OK

20. Mean Value Asymmetry known dead chamber with 2 noisy strips Not able to spot this kind NO More asymmetries are under Investigation taking into account the Necessity to minimize CPU usage

21. HV status from OMDS or ORCON OFF

22. Overall summary by roll • We now have the following set of parameters for each roll: • occupancy • cluster size mean • cluster size first bin • cluster size > 5 • % of dead strips • HV status • Asymmetry • Chamber categories: • OFF • dead • partially dead • noisy chamber • noisy strips • bad occup. Shape • OK TO BE DONE

23. Conclusion • We now have a new set of summary plots • 30 Roll .vs. Sector are all normalized • 30 distributions (per wheel) normalized • HV status from OMDS is now available (by chamber) • Many asymmetries method are under study to find the best way to spot “bad occupancy shape” • We are now ready to generate an high level summary plot (roll .vs sector) that should be the first plot to look at during the shift - Jan 09 • New algorithm for RPC summary is under studies (based on this new variables) - Feb 09 • Analysis can run both online and offline • Efficiency plot have been done in the same format but we should improve the code to run it in offline DQM • After that we will move to the offline DQM where a lot of work must be done in the next months.