1 / 25

CALORIMETER CELL MONITORING TOOL

CALORIMETER CELL MONITORING TOOL. Viacheslav Shary. INTRODUCTION. Goals: Detect the problems which were not found in the previous stages of the data quality check.

easter
Télécharger la présentation

CALORIMETER CELL MONITORING TOOL

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. CALORIMETER CELL MONITORING TOOL Viacheslav Shary

  2. INTRODUCTION Goals: • Detect the problems which were not found in the previous stages of the data quality check. • Provide the data quality information for further use: e.g. lists of cells, BLSs with unusual large or small signal. This information is of interest for the reconstruction and simulation programs and, of course, for the data analysis. CELL MONITORING TOOL

  3. THE SCHEME Raw calorimeter data from event distributor. Only ZERO BIAS events. Thumbnail calorimeter data. Only ZERO BIAS events. on-line rcp file off-line rcp file dq_calo package text report file (for shifter) input file for the database file with root trees CELL MONITORING TOOL

  4. DATA QUALITY (DQ) CHECK EVENT DQ CHECK N cells, total energy, total squared energy MET, SET CELL DQ CHECK (per run) N entries, total energy, total squared energy Fraction of bad events i.e. events with parameters out of limits bad cells and BLSs, i.e. channels with energy and/or RMS out of limits Run data quality: GOOD, REASONABLE, BAD Lists: bad cells, bad BLSs, BLSs without signal CELL MONITORING TOOL

  5. BLS QUALITY CRITERIA BLS is BAD if • mean energy E< -1.5 GeV or E > 2.0 GeV; • RMS < 0.1 GeV or RMS > 2.5 GeV; • more than 5 bad cells in the BLS • bad cell have a mean energy E < -4 GeV or E > 5 GeV All limits can be changed via rcp file. CELL MONITORING TOOL

  6. RUN QUALITY CRITERIA • If percentage of BAD events > 10% the run quality is BAD. • Else if number of cells with mean energy out of limits > 24 the run quality is BAD. • Else if number of ZERO_BIAS events in the run < 100 the run quality is UNKNOWN. • Else if number of BLSs without signal > 1 the run quality is BAD. • Else if number of BLSs without signal > 0 the run quality is REASONABLE. • Else if number of BAD BLSs > 0 the run quality is REASONABLE. • Else if percentage of BAD events > 2% the run quality is REASONABLE. • Else the run quality is GOOD. CELL MONITORING TOOL

  7. OUTPUT EXAMPLES • WWW: • http://d0-france.in2p3.fr/WORKING_GROUPS/DQ/ • At this page the following information is available. • Run list with corresponding qualities • Run report with bad cells BLS list • 2d histogram for many runs CELL MONITORING TOOL

  8. An example of 2d (-) histograms. The Z axis corresponds to the mean energy per entry in each cell (GeV). CELL MONITORING TOOL

  9. An example of 2d (-) histograms. The Z axis corresponds to the mean energy per entry in each cell (GeV). CELL MONITORING TOOL

  10. An example of electronics 2d histogram. X is ADC number. Y is serial channel number. Z is the mean energy per entry in the cell (GeV). CELL MONITORING TOOL

  11. An example of electronics 2d histogram. X is ADC number. Y is serial channel number. Z is the mean energy per entry in the cell (GeV). CELL MONITORING TOOL

  12. An example of 2d (-) histograms. The Z axis corresponds to the mean energy per entry in each cell (GeV). CELL MONITORING TOOL

  13. RESULTS CELL MONITORING TOOL

  14. TYPICAL PROBLEMS (1) • Usually runs have 0-2% of bad events, but some runs have near 25%. These runs have 1 or 2 BLSs with a cell(s) with a huge signal. These runs should be declared BAD, because it can affect physics quantities. • Some runs have no signal from several BLSs. Quality - BAD. • Some runs have no signal from one BLS. Quality - REASONABLE. CELL MONITORING TOOL

  15. TYPICAL PROBLEMS (2) • There are runs with large signal in all BLSs in one crate during some time. Quality - BAD. • Runs have frequently the following problem: 1 or 2 BLSs with a large signal (average signal per entry 2-10 GeV and RMS near 2-10 GeV). Usually these BLSs are the same in several runs. This problem affects missing ET and probably other physics quantities. Quality - REASONABLE. CELL MONITORING TOOL

  16. Missing ET (Ecell>0, without CH) PEAK ~ 2 GeV, FWHM ~ 3 GeV FWHM/2.36 ~ 1.3 GeV CELL MONITORING TOOL

  17. MISSING ET FOR ZERO BIAS CELL MONITORING TOOL

  18. MET for good & reasonable runs GOOD QUALITY FWHM ~ 3 GeV REASONABLE QUALITY FWHM ~ 3.5 GeV CELL MONITORING TOOL

  19. MET for reasonable runs CORRECTION: BAD CELLS AND CELLS IN BAD BLS ARE NOT USED FOR MET CALCULATION AFTER CORRECTION BEFORE CORRECTION CELL MONITORING TOOL

  20. MET for good & correc. runs CORRECTED REASONABLE QUALITY GOOD QUALITY CELL MONITORING TOOL

  21. MET for good & reasonable runs REASONABLE & GOOD QUALITY REASONABLE QUALITY GOOD QUALITY CELL MONITORING TOOL

  22. MET for reasonable runs CORRECTION: BAD CELLS AND CELLS IN BAD BLS ARE NOT USED FOR MET CALCULATION AFTER CORRECTION BEFORE CORRECTION CELL MONITORING TOOL

  23. MET for good & correc. runs CORRECTED REASONABLE QUALITY GOOD QUALITY CELL MONITORING TOOL

  24. ISSUES & PERSPECTIVES (1) • How to organize the permanent monitoring and feedback with shifters ? • The package was tested online by Laurent. How to proceed further ? • In the output text file shifter can find an information about run problems (lists of bad cells and BLSs). • How to make the data quality information available ? • The runs quality database can be used to save lists of bad cells and BLSs. • The bad cells can be removed from calorimeter data chunk. CELL MONITORING TOOL

  25. ISSUES & PERSPECTIVES (2) • Now it’s time to try this tools! • i.e. one can compare analysis results for good and reasonable runs. • What would the next step for calorimeter data quality be ? • Are there other hardware failure that we can detect ? Shared energy problem ? Do we need to look at physics trigger to detect some problem ? • Possibly, one could use clusters (CellNN) from reco to assert the impact of hardware problems on physics. It could also help discover warm zones, local inefficiencies and so on. CELL MONITORING TOOL

More Related