1 / 42

Virgo hierarchical search

Virgo hierarchical search. S. Frasca Baton Rouge, March 2007. The Virgo periodic source search. Whole sky blind hierarchical search (P.Astone, SF, C.Palomba - Roma1) Targeted search (F.Antonucci, F. Ricci – Roma1) Binary source search (T.Bauer , J.v.d.Brand, S.v.d.Putten – Amsterdam).

ondrea
Download Presentation

Virgo hierarchical search

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. Virgo hierarchical search S. Frasca Baton Rouge, March 2007

  2. The Virgo periodic source search • Whole sky blind hierarchical search (P.Astone, SF, C.Palomba - Roma1) • Targeted search (F.Antonucci, F. Ricci – Roma1) • Binary source search (T.Bauer , J.v.d.Brand, S.v.d.Putten – Amsterdam)

  3. The whole sky blind hierarchical search • Our method is based on the use of Hough maps, built starting from peak maps obtained by the SFTs.

  4. h-reconstructed data Here is a rough sketch of our pipeline Data quality SFDB Average spect rum estimation Data quality SFDB Average spect rum estimation peak map peak map hough transf. hough transf. candidates coincidences candidates coherent step events

  5. The whole sky blind hierarchical search • The software is described in the document at http://grwavsf.roma1.infn.it/pss/docs/PSS_UG.pdf

  6. Noise density for C7 (Sep 2005) and WSR9 (Feb 2007)(comparison with H1)

  7. Zoom at higher frequencies

  8. Creation of the SFTs, periodogram equalization and peak map construction • Time-domain big event removal • Non-linear adaptive estimation of the power spectrum (these estimated p.s. are saved together with the SFTs and the peak maps. • Only relative maxima are taken (little less sensitivity in the ideal case, much more robustness in practice)

  9. Example of n-l adaptive estimation of the power spectrum

  10. Time events: amplitude(C6 - 8109 events)

  11. Time events: duration (C6)

  12. Frequency events: time-frequency(C6 - 379960 spectral peaks)

  13. Frequency events: amplitude-duration

  14. Sampled data spectrum • Periodogram of 222 (= 4194304 ) data of C7

  15. Zoom of the periodogram

  16. Frequency histograms of the peaksC6

  17. Frequency histograms of the peaksC6 - Zoom

  18. Frequency histograms of the peaksC7

  19. After the comb filter Seconds in abscissa. Note on the full piece the slow amplitude variation and in the zoom the perfect synchronization with the deci-second.

  20. Spectrum after the comb filter

  21. 1kHz band analysis: peak maps • On the peak maps there is a further cleaning procedure consisting in putting a threshold on the peaks frequency distribution • This is needed in order to avoid a too much large number of candidates which implies a reduction in sensitivity. C7: peaks frequency distribution before and after cleaning

  22. The Hough map • Now we are using the “standard” (not “adaptive”) Hough transform • Here are the results

  23. Parameter space • observation time • frequency band • frequency resolution • number of FFTs • sky resolution • spin-down resolution ~1013 points in the parameter space are explored for each data set

  24. Candidates selection • On each Hough map (corresponding to a given frequency and spin-down) candidates are selected putting a threshold on the CR • The choice of the threshold is done according to the maximum number of candidates we can manage in the next steps of the analysis • In this analysis we have used • Number of candidates found: • C6: 922,999,536 candidates • C7: 319,201,742 candidates

  25. 1kHz band: candidates analysis C6: frequency distribution of candidates (spin-down 0) f [Hz]

  26. C6: frequency distribution of candidates (spin-down 0) f [Hz] peaks frequency distribution Sky distribution of candidates (~673.8Hz)

  27. C6: frequency distribution of candidates (spin-down 0) f [Hz] Sky distribution of candidates (~980Hz) peaks frequency distribution

  28. C6: frequency distribution of candidates (spin-down 0) f [Hz] Sky distribution of candidates (881-889Hz) peaks frequency distribution

  29. C7: frequency distribution of candidates (spin-down 0) f [Hz] Sky distribution of candidates (779.5Hz) peaks frequency distribution

  30. red line: theoretical distribution

  31. ‘disturbed’ band Many candidates appear in ‘bumps’ (at high latitude), due to the short observation time, and ‘strips’ (at low latitude), due to the symmetry of the problem ‘quiet’ band

  32. Coincidences • To reduce the false alarm probability; reduce also the computational load of the coherent “follow-up” • Done comparing the set of parameter values identifying each candidate • Coincidence windows: • Number of coincidences:2,700,232 • False alarm probability: band 1045-1050 Hz

  33. Software injections

  34. Software injections

  35. Software injections

  36. ‘Mixed data’ analysis • Let us consider two set of ‘mixed’ data: A6 B6 A7 B7 C6 C7 time • Produce candidates for data set A=A6+A7 • Produce candidates for data set B=B6+B7 • Make coincidences between A and B • Two main advantages: • larger time interval -> less ‘bunches’ of candidates expected • easier comparison procedure (same spin-down step for both sets)

  37. Log file data (crea_sfdb_ ...) • In any log file there are mainly: comments, parameters, “events” and statistics. • These are the log files of the SFDB construction • There are information on big time events and big frequency lines (as “events”)

  38. Log file “comments” • File D:\SF_DatAn\pss_datan\Reports\crea_sfdb_20060131_173851.log • started at Tue Jan 31 17:38:51 2006 • even NEW: a new FFT has started • PAR1: Beginning time of the new FFT • PAR2: FFT number in the run • even EVT: time domain events • PAR1: Beginning time, in mjd • PAR2: Duration [s] • PAR3: Max amplitude*EINSTEIN • even EVF: frequency domain events, with high threshold • PAR1: Beginning frequency of EVF • PAR2: Duration [Hz] • PAR3: Ratio, in amplitude, max/average • PAR4: Power*EINSTEIN**2 or average*EINSTEIN (average if duration=0, when age>maxage) • stat TOT: total number of frequency domain events • par GEN: general parameters of the run • GEN_BEG is the beginning time (mjd) • GEN_NSAM the number of samples in 1/2 FFT • GEN_DELTANU the frequency resolution • GEN_FRINIT the beginning frequency of the FFT • EVT_CR is the threshold • EVT_TAU the memory time of the AR estimation • EVT_DEADT the dead time [s] • EVT_EDGE seconds purged around the event • EVF_THR is the threshold in amplitude • EVF_TAU the memory frequency of the AR estimation • EVF_MAXAGE [Hz] the max age of the process. If age>maxage the AR is re-evaluated • EVF_FAC is the factor for which the threshold is multiplied, to write less EVF in the log file • stop at Wed Feb 1 12:39:22 2006

More Related