1 / 43

Optimization of SUPERCUTS using ON-OFF data

Optimization of SUPERCUTS using ON-OFF data. OUTLINE 1- Basic algorithm 2- Implementation in the MARS environment 3- Results obtained for Mkn 421 Data (Feb 15) 3.1 Static Cuts 3.2 Dynamic cuts (only in SIZE so far…) 4- Results obtained for Crab data (Jan 27th so far…).

kermit-miranda
Download Presentation

Optimization of SUPERCUTS using ON-OFF data

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. Optimization of SUPERCUTS using ON-OFF data OUTLINE 1- Basic algorithm 2- Implementation in the MARS environment 3- Results obtained for Mkn 421 Data (Feb 15) 3.1 Static Cuts 3.2 Dynamic cuts (only in SIZE so far…) 4- Results obtained for Crab data (Jan 27th so far…) David Paneque, MPI Muenchen

  2. 1- Basic algorithm 1) Application of INITIAL SUPERCUTS values to the data 2) Compute variable to estimate the goodness of these set of CUTS Significance, Nex, Q factor, F(Significance, Nex, Q)… 3) Modify CUTS 4) Apply new set of CUTS to the data 5) Compute variable to estimate the goodness of the CUTS and compare with the previous one 6) Repeat points 3,4,5 till the estimator reaches a maximum David Paneque, MPI Muenchen

  3. 2- Implementation in the MARS environment 2.1- Introduction Done by Wolfgang half a year ago (only ON data used) New classes done by myself to use ON-OFF data (finished at mid February) Basic tools (polynomial fits, Minuit interface for the minimization… ) are the same, and were/are working reliably. A small set of new functionalities were added Possibility to optimize in different bins in zenith angle, and combine (a posteriori) the results Storage of all info (alpha plots, normalization factors, shower parameters, cuts… ) in a root file Usage or static/dynamical cuts (use/not use theta, dist…) David Paneque, MPI Muenchen

  4. 2- Implementation in the MARS environment 2.2- Working principle Within class MFindSupercutsONOFF 1) INITIAL SET of CUTS are given (by user) and stored in container MSupercuts Cuts are computed (in case of dynamical cuts) and applied to data through class MSupercutsCalc 3) Significance is computed using class MHFindSignificanceONOFF LiMa (1984) formula 17 used  = Non/Noff (about 2.5 for Mkn421) strictly mathematically correct Error in Noff is obtained from fit, and is usually smaller than sqrt(Noff). Effective  is about 1 strictly mathematically correct David Paneque, MPI Muenchen

  5. 2- Implementation in the MARS environment 2.2- Working principle 4) Parameters are modified to maximize significance using the class MMinuitInterface Final (optimized) parameters are written into container MSupercuts, and applied to the data producing the alpha plots and computing final significance and Nex 6) Alpha Plots stored in postscript files and inside a root file together with other information (normalization factors, significance, Nex, hillas parameters, cuts…) for a later study Class MFindSupeructsONOFFThetaloop used to run MFindSupercutsONOFF separately in different ZENITH angle bins (eventually also SIZE bins), and combining results (if desired) at the end. Besides, this is also the class defining names of root data files, histograms were to store info (Signfinicance, Nex…) and saving all those guys into single root file David Paneque, MPI Muenchen

  6. 2- Implementation in the MARS environment 2.3- How to use these programs Just by defining some variables in a silly macro… David Paneque, MPI Muenchen

  7. 3- Results obtained for Mkn 421 data (Feb15) 3.0 - Data preprocessing Calibration 1) Use of MExtractSignal2 (Slide window method) 2) Rejection of bad calibration runs (manually) 3) Set conversion factors to ZERO to those pixels for which a) <Q> < 50 ADC counts b) Reduced_Sigma/<Q> 4 sigmas away from the mean away from the mean of the distribution for all pixels (done separately for inner/outer pixels) c) Data is calibrated using the closest calibration run (script written by Robert Wagner) David Paneque, MPI Muenchen

  8. 3- Results obtained for Mkn 421 data (Feb15) 3.0 - Data preprocessing Image cleaning 1) Blind pixels signals are interpolated (when possible) as explained by Nadia yesterday 2) Cleaning levels set to HIGH values: 4 Sigmas for core and neighbouring pixels Minimum of 4 core pixels and VERY HIGH values: 6 Sigmas for core pixels 4 Sigmas for neighbouring pixels Minumum of 6 core pixels 3 Rings of neighbouring pixels Comparison ON-OFF data BEFORE APPLYING CUTS David Paneque, MPI Muenchen

  9. STRONG image cleaning vs VERY STRONG image cleaning Noise or real Signal ??? 3- Results obtained for Mkn 421 data (Feb15) 3.0 - Data preprocessing VERY Strong cleaning removes more noise, and therefore images “look cleaner”; LARGER ACCEPTANCE IN THE CUTS I might be too conservative… Yet I feel better playing with the VERY STRONG CLEANING 6 Sigmas for core pixels 4 Sigmas for neighbouring pixels Minumum of 6 core pixels 3 Rings of neighbouring pixels VERY Strong cleaning also removes islands from hadrons, and hence, decreases the separation power; LARGER ACCEPTANCE IN THE CUTS ALSO FOR HADRONS David Paneque, MPI Muenchen

  10. UNFOLDING of source movement by means of FALSE SOURCE ANALYSIS in sub-samples (6-24) ordered chronologically (Method explained by Daniel yesterday…) 3- Results obtained for Mkn 421 data (Feb15) 3.0 - Data preprocessing Iterative process, as already explained yesterday Image cleaning used in 2D analysis was 4 Sigmas for core pixels 3 Sigmas for neighbouring pixels Minumum of 4 core pixels 3 Rings of neighbouring pixels David Paneque, MPI Muenchen

  11. 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Static cuts (size cut 3000 ph) Parameters optimization in TRAIN sample; and tested in TEST sample Nex = 536 +/- 41 (13.1 Sig) Width = 5.5 +/- 0.4 deg Nex = 517 +/- 40 (13.0 Sig) Width = 5.1 +/- 0.4 deg David Paneque, MPI Muenchen

  12. 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Static cuts (size cut 3000 ph) Optimized parameters applied in WHOLE SAMPLE OF DATA David Paneque, MPI Muenchen

  13. Nex = 1028 +/- 56 (18.2 Sigmas) Width = 5.3 +/- 0.3 deg Rate = 9.7 +/- 0.5 events/min 0.124 < LENGTH < 0.35 0.055 < WIDTH < 0.125 0.60 < DIST < 1.25 L/W > 1.5 David Paneque, MPI Muenchen

  14. 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Static cuts (size cut 2000 ph) Parameters optimization in TRAIN sample; and tested in TEST sample Nex = 750 +/- 57 (13.1 Sig) Width = 5.4 +/- 0.4 deg Nex = 821 +/- 57 (14.4 Sig) Width = 5.6 +/- 0.4 deg David Paneque, MPI Muenchen

  15. 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Static cuts (size cut 2000 ph) Optimized parameters applied in WHOLE SAMPLE OF DATA David Paneque, MPI Muenchen

  16. Nex = 1497 +/- 75 (20.0 Sigmas) Width = 5.7 +/- 0.3 deg Rate = 14.1 +/- 0.7 events/min 0.12 < LENGTH < 0.34 0.054 < WIDTH < 0.115 0.59 < DIST < 1.25 L/W > 1.5 David Paneque, MPI Muenchen

  17. Usage of ON events that pass the cuts (Hadroness < 0.5) and with an ALPHA < 6 degrees 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Dynamical cuts (only in SIZE so far…) Necessity of INITIAL SET OF DYNAMICAL CUTS PROBLEM: New MC still does not describe perfectly the data SOLUTION: Use ON data to “parameterize” SIZE dependence on image parameters (LENGTH, WIDTH, DIST ) of GAMMAS David Paneque, MPI Muenchen

  18. Hadroness < 0.5 && ALPHA < 6 deg LENGTH vs Ln (SIZE) Slope = 0.034 Ln (SIZE) David Paneque, MPI Muenchen

  19. Hadroness > 0.5 LENGTH vs Ln (SIZE) Slope = 0.053 Ln (SIZE) David Paneque, MPI Muenchen

  20. Hadroness < 0.5 && ALPHA < 6 deg WIDTH vs Ln (SIZE) Slope = 0.012 Ln (SIZE) David Paneque, MPI Muenchen

  21. Hadroness > 0.5 WIDTH vs Ln (SIZE) Slope = 0.041 Ln (SIZE) David Paneque, MPI Muenchen

  22. Hadroness < 0.5 && ALPHA < 6 deg DIST vs Ln (SIZE) Slope = 0.059 Ln (SIZE) David Paneque, MPI Muenchen

  23. Hadroness > 0.5 DIST vs Ln (SIZE) Slope = 0.21 Ln (SIZE) David Paneque, MPI Muenchen

  24. Usage of ON events that pass the cuts (Hadroness < 0.5) and with an ALPHA < 6 degrees Parameterization of the DYNAMICAL CUTS Dynamical CUT_i= Static CUT_i + a LnS + b (LnS)2 LnS = Ln (SIZE) - Ln (SIZE_OFFSET) 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Dynamical cuts (only in SIZE so far…) Necessity of INITIAL SET OF DYNAMICAL CUTS PROBLEM: New MC still does not describe perfectly the data SOLUTION: Use ON data to “parameterize” SIZE dependence on image parameters (LENGTH, WIDTH, DIST ) of GAMMAS Static cut is fixed to the value obtained before David Paneque, MPI Muenchen

  25. 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Dynamical cuts (size cut 3000 ph) Parameters optimization in TRAIN sample; and tested in TEST sample Nex = 514 +/- 34 (15.1 Sig) Width = 5.3 +/- 0.4 deg Nex = 548 +/- 35 (15.6 Sig) Width = 4.7 +/- 0.3 deg David Paneque, MPI Muenchen

  26. 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Dynamical cuts (size cut 3000 ph) Optimized parameters applied in WHOLE SAMPLE OF DATA David Paneque, MPI Muenchen

  27. Nex = 1043 +/- 49 (21.3 Sigmas) Width = 5.0 +/- 0.2 deg Rate = 9.8 +/- 0.5 events/min David Paneque, MPI Muenchen

  28. 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Dynamical cuts (size cut 2000 ph) Parameters optimization in TRAIN sample; and tested in TEST sample Nex = 759 +/- 42 (17.9 Sig) Width = 5.3 +/- 0.3 deg Nex = 710 +/- 43 (16.6 Sig) Width = 5.6 +/- 0.4 deg David Paneque, MPI Muenchen

  29. 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Dynamical cuts (size cut 2000 ph) Optimized parameters applied in WHOLE SAMPLE OF DATA David Paneque, MPI Muenchen

  30. Nex = 1461 +/- 60 (24.3 Sigmas) Width = 5.5 +/- 0.3 deg Rate = 13.8 +/- 0.6 events/min David Paneque, MPI Muenchen

  31. 3- Results obtained for Mkn 421 data (Feb15) 3.1 - Dynamical cuts (size cut 3000 ph) Nice alpha plot when allowing “static cut” to be modified… David Paneque, MPI Muenchen

  32. Nex = 816 +/- 37 (21.7 Sigmas) Width = 5.3 +/- 0.2 deg Rate = 7.7 +/- 0.3 events/min Image cleaning used here was not that strong 4 Sigmas for core pixels 4 Sigmas for neighbouring pixels Minumum of 4 core pixels David Paneque, MPI Muenchen

  33. 3- Results obtained for Mkn 421 data (Feb15) 3.2 - Dynamical cuts (size cut 3000 ph) Parameters optimization in TRAIN sample; and tested in TEST sample Nex = 440 +/- 22 (20.0 Sig) Width = 4.9 +/- 0.3 deg Nex = 401 +/- 26 (15.5 Sig) Width = 5.7 +/- 0.4 deg David Paneque, MPI Muenchen

  34. 3- Results obtained for Crab data (Jan27) 3.2 - Static cuts (size cut 3000 ph) No OPTIMIZATION; application of optimized (static) parameters with Mkn421 Estimated position for Crab (camera coordinates): X = 0.05 Y = - 0.20 David Paneque, MPI Muenchen

  35. Nex = 199 +/- 31 (6.4 Sigmas) Width = 12.6 +/- 2.6 deg Rate = 4.0 +/- 0.6 events/min 0.124 < LENGTH < 0.35 0.055 < WIDTH < 0.125 0.60 < DIST < 1.25 L/W > 1.5 David Paneque, MPI Muenchen

  36. 3- Results obtained for Crab data (Jan27) 3.2 - Dyn cuts (size cut 3000 ph) No OPTIMIZATION; application of optimized (dynamic) parameters with Mkn421 David Paneque, MPI Muenchen

  37. Nex = 242 +/- 38 (6.4 Sigmas) Width = 16.5 +/- 3.0 deg Rate = 4.8 +/- 0.8 events/min David Paneque, MPI Muenchen

  38. 3- Results obtained for Crab data (Jan27) 3.2 - Dyn cuts (size cut 2000 ph) No OPTIMIZATION; application of optimized (dynamic) parameters with Mkn421 David Paneque, MPI Muenchen

  39. Nex = 297 +/- 46 (6.5 Sigmas) Width = 17.1 +/- 3.4 deg Rate = 6.0 +/- 0.9 events/min David Paneque, MPI Muenchen

  40. Status of the detections Crab from 27 Jan needs some more work to understand why signal Is so wide… are we really catching the position of Crab in the Camera ?? Crab from 15th Feb; need some time to “unfold” its movement in the camera… and then to analyze it… Mkn 421 movement in the camera has been (up to some extent) “unfolded” and cuts (static and dynamic in size) successfully applied. Dependence with DIST parameter will be included soon. Very clear detection. It ”showed” us many problems in the telescope Telescope pointing and/or AMC PMTs Gain oscillations Signal jitter in FADC slices “Strange” SIZE distribution (High/low gain??) Through the analysis of these strong signals we can improve the telescope performance David Paneque, MPI Muenchen

  41. Policy to commit software to CVS should be discussed Quite some people are working with software not committed to the CVS. WHY ? My experience: I was not allowed to commit into CVS classes to apply SUPERCUTS using ON-OFF data because Names were not appropriate 2) Not fully object oriented programmed; there is code which is partly existing already in CVS 3) Software committed to CVS should be such that future updates (ECO 1000) are possible David Paneque, MPI Muenchen

  42. Policy to commit software to CVS should be discussed In my opinion, there are more important criteria that should be considered when committing things to the CVS Code MUST compile New code (changes in the code) MUST NOT AFFECT functionality of other classes Before changing code, CONTACT author of code and authors of classes using such code to discuss impact of modifications 3) New code MUST produce RELIABLE (up to some extent) results. Usually people use it without knowing how it works. It is worth to spend some time “playing” with the code before committing. 4) New code should be easy to read and be uded, even for not C++ experts…. David Paneque, MPI Muenchen

  43. Policy to commit software to CVS should be discussed In order to increase efficiency and reliability of our software, CLEAR and OBJECTIVE rules should be defined and accomplish by everybody. This is a good place and a good moment to discuss about it… David Paneque, MPI Muenchen

More Related