1 / 20

SGR 1806-20 Giant Flare and Gravitational Wave emission

SGR 1806-20 Giant Flare and Gravitational Wave emission. Coincidences data analysis using cross correlation techniques. SGR 1806-20 Giant Flare. 27 Dec. 2004 Giant Flare SGR 1806-20 Double structure: precursor (at -142 s.) and flare

eara
Download Presentation

SGR 1806-20 Giant Flare and Gravitational Wave emission

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. SGR 1806-20 Giant Flare and Gravitational Wave emission Coincidences data analysis using cross correlation techniques Roberto Terenzi (ROG)

  2. SGR 1806-20 Giant Flare • 27 Dec. 2004 Giant Flare SGR 1806-20 • Double structure: precursor (at -142 s.) and flare • Search for gravitational wave emission (gwe) in coincidences with flare and precursor Roberto Terenzi (ROG)

  3. Flare and precursor (from Nature) - ±10s, i.e. ±10s, i.e. ±10s, i.e.

  4. Data Analysis: coincidences between electromagnetic emission and gwe • Coincidences windows=± 10 s (Magnetar spin period=7.56s, see Nature) • Both at flare and at precursor • Analysis technique : cross correlation coefficient • For two sets of data {x} and {y}: Roberto Terenzi (ROG)

  5. Cross correlation coefficient parameters settings • Lag: it depends on time of fly and detectors electronics delays. • Correlation interval: it depends on signal time length (1s.) • Correlation step (k): it depends on how much time resolution in cross correlation coefficients we would like to have (20 ms) Roberto Terenzi (ROG)

  6. Time of fly and lag • A gravitational wave (gw) coming from the Magnetar on the 27 Dec. 2004 at 21h:30m:26.68s should hit first Explorer and then Nautilus with a time delay of the order of 1250 μs. • In Dec. 2004 the Explorer and Nautilus electronics are different: they present an input-output signal difference in delay time of the order of 650 μs . • Nominal lag= (1250-650) ± 300 μs., direction: Explorer data sequence should be delayed before cross correlate with Nautilus data sequence of about 600μs ± 300 μs due to data timing uncertainties Roberto Terenzi (ROG)

  7. Data • 1. Detector output data orraw data: data collected at the end of the detector electronics acquisition chain, without any further conditioning. • 2. Detector input data orinput data or deconvolved data: the detector output data deconvolved by the detector transfer function to get the input strain h. • 3. White or whitened data: the output of a whitening process on the input data that re-normalize the noise. • 4. Filtered or wavelets filtered data: the output of an EWF applied to the whitened data. Roberto Terenzi (ROG)

  8. Input Data Roberto Terenzi (ROG)

  9. Data Analysis Pipeline 1 data flow • 1. Detectors raw data deconvolved by detectors transfer function input data (h) • 2. Input data spectrum module averaged reference whitening spectrum • 3. Chunk of input data divided by reference whitening spectrum whitened data • 4. Whitened data filtered by EWF filtered data • 5. Filtered data Cross correlation coefficients (ccc) Roberto Terenzi (ROG)

  10. Data Analysis Pipeline 2.1:ccc data processing • Q.: In a ± 10s coincidence window how the candidate coincidence timing between electomagnetic emission and gwe can be defined? • A.: With 1 detector only, by choosing the time of the maximum of the signal in the window, for example. With two detectors….. Roberto Terenzi (ROG)

  11. Data Analysis Pipeline 2.2 • A.: With TWO detectors, we can select the time of the 1s sub-interval showing the maximum cross correlation coefficient between the two detectors data. Roberto Terenzi (ROG)

  12. Data Analysis Results 4: Cross Correlation coefficient values:4h data, 200ms Roberto Terenzi (ROG)

  13. Data Analysis Results 1: Cross Correlation coefficient values Roberto Terenzi (ROG)

  14. Data Analysis Results 3: Cross Correlation coefficient values Whitened and Filtered data Roberto Terenzi (ROG)

  15. Data Result: 0.275,0.282 ?? • Statistics on ccc: • 4 hours of data cross correlated • Histogram and Gaussian Fit • Frequency of occurrence of data value found in the distribution fit • Probability, given the two values, of having such values in the 2 coincidences windows • ENERGY of gwe needed to have such values (i.e. method calibration) Roberto Terenzi (ROG)

  16. Data Analysis Results 5: Max of Cross Correlation coefficient values in 4h data ± 10 s coincidence windows Roberto Terenzi (ROG)

  17. Data Analysis Results 5: Max of Cross Correlation coefficient values in 4h data ± 10 s coincidence windows Roberto Terenzi (ROG)

  18. Data Result: 0.275,0.282 ?? • Statistics on ccc: • 4 hours of data cross correlated • Histogram and Gaussian Fit • Frequency of occurrence of data value found in the distribution fit • Probability, given the two values, of having such values in the 2 coincidences windows • ENERGY of gwe needed to have such values (i.e. method calibration) Roberto Terenzi (ROG)

  19. Data Result: 0.275,0.282 ?? • Frequency of occurrence of data value found in the distribution fit: 3.38σ and 3.58σ • Probability, given the two values, of having such values in the 2 coincidences windows: p≈10^−5 Roberto Terenzi (ROG)

  20. Data Result: 0.275,0.282 ?? • ENERGY of gwe needed to have such values (at precursor, hip: Magnetar at 15kpc): 7 ∗ 10^−4 M c^2 M= Solar mass Less at flare… Roberto Terenzi (ROG)

More Related