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VEGA Visual Environment for Gravitational waves data Analysis

VEGA is a specialized environment built upon ROOT for analyzing gravitational wave data, evaluated by the VIRGO project in Cascina, Italy. It simplifies data handling, employing a unique Frame format that captures temporal data from interferometers. The workshop discusses gravitational waves, their detection methods such as interferometry, and VEGA's functionalities, strengths, and challenges. Topics include data processing similarities with high-energy physics, data storage efficiency, signal analysis techniques, and user interaction. Aimed at enhancing understanding and accessibility, VEGA represents a key tool in astrophysics research.

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VEGA Visual Environment for Gravitational waves data Analysis

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  1. VEGAVisual Environmentfor Gravitational waves dataAnalysis D. Buskulic / LAPP Annecy ROOT 2000 Workshop

  2. Outlook • What is a Gravitational Wave ? • How to detect it ? Example : VIRGO • Data Handling : Similarities/Differences with HEP • VEGA : an environment based on ROOT • Strengths • Weaknesses and problems • Conclusion

  3. What is a Gravitational Wave ? • Very small deformations of space time • Amplitude h = DL/L < 10-21 (1 = flat space) • Travelling (waves) at the speed of light • Generated by cosmic events : • Supernovae, Coalescing binary neutron stars, Formation of black holes... • Gives new insights in fundamental Physics and Astrophysics

  4. How to detect it ? • One way : • Resonant bars • Another way : • Measure time of flight of a light beam : Michelson Interferometer

  5. How to detect it ? (II) • Principle: • Detect 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. Example : VIRGO • Located in Cascina, near Pisa, Italy • Arms length : 3 km • Sensitivity zone : 10 Hz-1 kHz • Sensitivity max : • Begins running in 2002

  7. Example : VIRGO (II)

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

  9. VEGA : an environment based on ROOT • Evaluated by the VIRGO experiment • User interaction : use ROOT facilities • Data access : the metadatabase • Signal processing/analysis • Graphics

  10. VEGA : the metadatabase • Data stored in Frame format • Time is the main access parameter • Need to access simply any vector/frame • Build a database containing metadata (data about data) • Indexes a set of Frame files

  11. VEGA : the metadatabase (II)

  12. VEGA : the metadatabase (III) • Performances : • Tested with 105 files, 1 frame each • If 100 frames/file, simulates 107 frames in terms of memory and speed (3 months of VIRGO data) • Memory : ~ 500 MB to index ~ 1 TB of data • Speed : Metadatabase overhead in access time = 2% of frame access time on average

  13. VEGA : Signal processing/analysis • Plan to use VIRGO data analysis library • Basic signal analysis • FFT, Convolution, Filters • Standard VIRGO analysis tools for GW signals • Need 100s of GFlops of computing power, use of PROOF ?

  14. VEGA : Graphics • Time is everywhere in GPS format : • Today 14:00:00 is GPS 633618013 ! • Need a reference time • Added time on the axis into ROOT • Not perfect but works • Use of plots with large number of points (>105)

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

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

  17. What people dislike (II) • The Interpreter • Sometimes dereferencing 0 gives a segfault • If segfault, no core, no info where it happened (macro line) • But less complaints about the interpreter • Graphics • Axis on the right are not intuitive to draw-> Doc in TGaxis, people search in TAxis

  18. What people dislike (III) • The class doc contains almost everything but… • Finding a piece of info is not trivial • No reference where to search • Examples • Greek letters-> TPostscript or TLatex or TText ? • Axis -> TGaxis or TAxis ? • You have to know the name of the class, not trivial for beginners • Sometimes three classes to find one info

  19. What people like • Tab completion • Interactivity (zoom, changing properties of objects…) • The Interpreter • … and many more !

  20. Strengths of ROOT for us • We hope connection with batch is simplified thanks to the interpreter • Frame access is fast through the metadatabase • Keep the Frame format • Consistency across the whole data analysis

  21. All in All... • VEGA is based on ROOT, and happy like this ! • Also based on the Framelib managing Frame files • Consistency through the analysis • For the language • For the input/output http://wwwlapp.in2p3.fr/virgo/vega

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