1. VEGA Visual Environment for Gravitational waves data Analysis D. Buskulic / LAPP Annecy ROOT 2000 Workshop

2. Outlook zWhat is a Gravitational Wave ? zHow to detect it ? Example : VIRGO zData Handling : Similarities/Differences with HEP zVEGA : an environment based on ROOT zStrengths zWeaknesses and problems zConclusion

3. What is a Gravitational Wave ? zVery small deformations of space time  Amplitude h =  L / L < 10 -21 (1 = flat space) zTravelling (waves) at the speed of light zGenerated by cosmic events : ySupernovae, Coalescing binary neutron stars, Formation of black holes... zGives new insights in fundamental Physics and Astrophysics

4. How to detect it ? zOne way : yResonant bars z Another way : yMeasure time of flight of a light beam : Michelson Interferometer

5. How to detect it ? (II) zPrinciple: zDetect the difference in light travel time between the two arms (interferometry) GW travelling along z axis changes distance in x and y : change in x is opposite to change in y and oscillating

6. zLocated in Cascina, near Pisa, Italy zArms length : 3 km zSensitivity zone : 10 Hz-1 kHz zSensitivity max : zBegins running in 2002 Example : VIRGO

7. Example : VIRGO (II)

8. Data Handling : Similarities/Differences with HEP zData flux : 7.6 MB/s -> 150 - 200 TB/year (few channels, continuous recording) zOnline selection -> 3 TB of selected data zMain difference with HEP : no events, but data have a temporal link zDefinition of a specific data format : the Frame z1 Frame = data chunk of 1 sec of interferometer running

9. VEGA : an environment based on ROOT zEvaluated by the VIRGO experiment zUser interaction : use ROOT facilities zData access : the metadatabase zSignal processing/analysis zGraphics

10. VEGA : the metadatabase zData stored in Frame format yTime is the main access parameter zNeed to access simply any vector/frame zBuild a database containing metadata (data about data) zIndexes a set of Frame files

11. VEGA : the metadatabase (II)

12. VEGA : the metadatabase (III) zPerformances : yTested with 10 5 files, 1 frame each yIf 100 frames/file, simulates 10 7 frames in terms of memory and speed (3 months of VIRGO data) yMemory : ~ 500 MB to index ~ 1 TB of data ySpeed : Metadatabase overhead in access time = 2% of frame access time on average

13. VEGA : Signal processing/analysis zPlan to use VIRGO data analysis library yBasic signal analysis xFFT, Convolution, Filters yStandard VIRGO analysis tools for GW signals zNeed 100s of GFlops of computing power, use of PROOF ?

14. VEGA : Graphics zTime is everywhere in GPS format : yToday 14:00:00 is GPS 633618013 ! zNeed a reference time zAdded time on the axis into ROOT yNot perfect but works zUse of plots with large number of points (>10 5 )

15. Weaknesses/Problems (T) = Technical problem z(T) Time on the axis still has problems, related to the way axis are drawn z(T) Numbers (time) on the axis grow rapidly yMay want to display 1 sec of data going from 1000000 to 1000001 xproblems in graphics in that case

16. What people dislike zInteractivity yFinding the right object is not natural (though logical) xFor one histogram -> five objects in a pad xEnhance popups with info on neighboring objects ? y(T) Histogram is hard to pick with mouse when large number of points yTPaveStats::SetStat(111) : Meaning of 111 in dialog… ? See point about doc and help

17. What people dislike (II) zThe Interpreter ySometimes dereferencing 0 gives a segfault yIf segfault, no core, no info where it happened (macro line) yBut less complaints about the interpreter zGraphics yAxis on the right are not intuitive to draw -> Doc in TGaxis, people search in TAxis

18. What people dislike (III) zThe class doc contains almost everything but… zFinding a piece of info is not trivial yNo reference where to search yExamples xGreek letters-> TPostscript or TLatex or TText ? xAxis -> TGaxis or TAxis ? yYou have to know the name of the class, not trivial for beginners ySometimes three classes to find one info

19. What people like zTab completion zInteractivity (zoom, changing properties of objects…) zThe Interpreter z… and many more !

20. Strengths of ROOT for us zWe hope connection with batch is simplified thanks to the interpreter zFrame access is fast through the metadatabase zKeep the Frame format zConsistency across the whole data analysis

21. All in All... zVEGA is based on ROOT, and happy like this ! zAlso based on the Framelib managing Frame files zConsistency through the analysis yFor the language yFor the input/output http://wwwlapp.in2p3.fr/virgo/vega