1. Atlantis tutorial Hans Drevermann (CERN), Janice Drohan (UCL), Charles Timmermans (KUN) 4 March 2004 ATLAS Software Week/CERN

2. Outline Introduction Data visualization – Hans Basic concepts (presentation) – Janice Hands on demonstration of basic functionality User session ---- Coffee ---- Access to events – Janice Advanced features (presentation) - Charles Hands on demonstration of advanced features Questions & answers

3. Atlantis goals Primary visual investigation and physical understanding of complete Atlas events. Secondary help develop reconstruction and analysis algorithms debugging during commissioning pictures and animations for publications, presentations and exhibitions event display for simple test-beams online event display

4. Data The following data may currently be visualized by the program 3D silicon points, silicon strip clusters and TRT straws Si Geant Hits, Trigger space points Simulated tracks, neutral particles and vertices Reconstructed tracks iPatRec, xKalman, IDScan Reconstructed secondary vertices LAr, TILE, HEC and FCAL calorimeter cells and clusters. MDT, RPC, TGC, CSC hits and CSC clusters Simulated and reconstructed muon tracks (MOORE) Associations (hit to track, cell to cluster etc)

5. Detector/Data oriented projections 3D Cartesian coordinates x,y,z are not always optimal for colliding beam experiments More natural and useful are the non-linear combinations which reflect the design of ATLAS  =  (x 2 +y 2 ),  = atan2(y, x),  = log( z/  (z/  2 +1)  where x, y, z need to be slightly modified to take into account the primary vertex of the underlying event (x vtx y vtx z vtx ) x' = x-x vtx, y' = y-y vtx, z' = z-z vtx

6. Y/X projection – TILE, LAr barrel, RPC (intuitive) RPC

7.  projection – like Y/X, but prompt tracks are straight lines

8.  projection – calorimeters, muon hits (  sector ) (intuitive)

9. X'  projection – muon hits and their association to Moore tracks

10.  projection – TRT and LAr endcaps, HEC, TGC phi strips TRT TGC

11.  projection and the V-Plot    Max  cm V-PlotDraw each space point twice at  +k*(  Max ) and  -k*(  Max )  3D information For tracks can judge   pt (slope of V arms) charge (  -ve V +ve) Distorted V’s track not from IP low p, -ve high p, +ve

12.  projection – track to calorimeter associations (30 GeV electron) Pt=29.3 GeV E =31.2 GeV LAr Presampler LAr Layer 1 LAr Layer 2LAr Layer 3 Island (guides eye) Track (enters LAr here) Cell geometry  Area  E

13. User interface Menu bar (IO,preferences,help) Window Control (zoom,copy, DnD) Commands Output window Parameter groups Interaction Control Parameters

14. Online help – available for every component Right click on component for online help (hyper-linked HTML) Hover for tool-tip help

15. Interactions ZMR - zoom, move and rotate w.r.t defined center Rubberband - selection and zooming Pick - pick and move to (selection and query) Fisheye - relative expansion of central region Clock - relative expansion of angular region Synchro-cursors - correlation between different projections Scale - copy scales between windows Mostly mouse driven with sometimes a modifier key pressed on the keyboard

16. Input Data Atlantis is a JAVA application It communicates with Athena via dedicated XML files produced by JiveXML ( see talk later) These files are best grouped and compressed inside zip files Single design luminosity event is approx 20 MB (XML) 4 MB (zip)

17. Detector Geometry Used to convey quickly to the user the context in which hits are to be viewed. Idealized geometry is adequate and desirable. (e.g. LAr pre-sampler is only 1 cm thick and would be invisible if drawn as such Stored in two separate XML files. muon geometry derived from parameter book.

18. Printing File => print => EPS, PNG, GIF EPS – high quality vector graphics good for posters, publications (file size 200KB - 2MB) PNG – compressed bitmap good for ppt presentations + web (file size 20-50 KB)

19. AtlFast

20. User defined geometry (e.g. MDT - cosmic test stand)

21. Web page www.cern.ch/atlantis How to download, install and run Atlantis Picture database (example event displays) Presentations

22. Contributors Many people contributed to the development of Atlantis. In particular: Gary Taylor (UC Santa Cruz) Principal developer Hans Drevermann (CERN/EP) Original ideas, FORTRAN version Dumitru Petrusca (Siegen/CERN) Initial work on GUI, calorimeters Jon Couchman (UCL) Athena algorithm (JiveXML) Frans Crijns (Nijmegen) Muon geometry Peter Klok (Nijmegen) Picture database Current Developers: Janice Drohan (UCL) Charles Timmermans (Nijmegen)

23. Atlantis tutorial-2 Hans Drevermann (CERN), Janice Drohan (UCL), Charles Timmermans (KUN) 4 March 2004 ATLAS Software Week/CERN

24. Analysis Techniques Data to be viewed may be Cut - e.g. by pT, energy, association… Colored - by associations, layer, sub-detector more powerful when used in combination e.g. selected only hits belonging to kine tracks and color them by their associated reconstructed track ( inconsistencies indicate problems) Superimposed – iPatRec tracks over true tracks

25. Check track reconstruction in difficult design luminosity event Selected event has two high p t (>560GeV) jets ( DC1dataset 2045) Luminosity 10 34 Silicon space points 27,000 TRT hits 240,000 Reconstructed tracks 120 Reconstructed in 20 minutes

26. 2D projections of Inner Detector data not very useful at design luminosity (TRT -ve barrel only)

27. V-Plot silicon space points  calorimeters

28. Filtering of space points available inside Atlantis Filter space points with a histogram based technique which selects hits consistent with tracks originating from the primary vertex. Time = 1 sec/event ATLAS note in preparation

29. Filtered hits iPatRec tracks True tracks 36 tracks 440 GeV 34 tracks 410 GeV 25 tracks 222 GeV 27 tracks 270 GeV

30. Tracks lost in core of central jet STr,iPat,S3D(STr,iPat)iPat, xKal iPat,S3D(Filter,iPat)

31. Lists Up till now we have seen how to investigate data and association present on the input file. Lists allow user to dynamically create and manage their own associations grouping of object perform context dependent operations e.g. vertex a set of reconstructed tracks

32. Identifying secondary vertices Look for a group of nearby kinked V’s in the VPlot Reconstructed tracks True tracks D B

33. Y/X projection – region around the primary vertex Region around primary vertex Reconstructed tracks True tracks Primary B D 3  error ellipse

34. Secondary vertex region best displayed in abstract 3D Box Plane containing primary vertex Plane containing secondary vertex primary vertex Ellipses represent track error (1  ) secondary vertex

35. Comparison of muon and inner detector track fits V-Plot allows comparison of  and p t p = 28 GeV  p = 5 GeV  = 1.5 deg  = 0.02 p = 25 GeV  p = 4 GeV  = 1.0 deg  = 0.01   Inner detector tracks muon tracks

36. Cell clustering and Jet reconstruction – AtlFast (DC1- QCD event) Cells coloured by cluster (Area  E) Jet (Area  E) E= 13 GeV  E= 347 GeV Details of cell clustering ? Bug ?