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Update on the analysis of VSR1 data, focusing attention on the presence of spectral disturbances.

Analysis of spectral lines in VSR1 pulsar search F. Antonucci, P. Astone, S. D’Antonio, S. Frasca, C. Palomba. Update on the analysis of VSR1 data, focusing attention on the presence of spectral disturbances. More data  more details can be seen

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Update on the analysis of VSR1 data, focusing attention on the presence of spectral disturbances.

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  1. Analysis of spectral lines in VSR1 pulsar search F. Antonucci, P. Astone, S. D’Antonio, S. Frasca, C. Palomba • Update on the analysis of VSR1 data, focusing attention on the presence of spectral disturbances. • More data  more details can be seen • We are interested in identifying disturbances of clear or likely instrumental origin in order to remove them. This brings to a reduction in the number of candidates found in the analysis (and possibly allows for a reduction in the threshold for candidate selection). • Commissioning and noise people can be also interested. C. Palomba - DA Meeting 29-1-2008

  2. The analysis/cleaning of the spectral disturbances is done at the level of peak maps. We will also see how they reflect in candidate distribution Scheme of the DA pipeline calibrated data Data quality SFDB Average spect rum estimation Data quality SFDB Average spect rum estimation The peak map is built from the ratio between periodograms and the corresponding estimations of the average spectrum, selecting local maxima above a given threshold We look at the frequency distribution of peaks peak map peak map hough transf. hough transf. candidates coincidences candidates coherent step events

  3. persistency • Three main categories of disturbances: • - ‘known’ Virgo lines (i.e present in the list); • - lines of likely instrumental origin (mainly harmonics of a set of frequencies); • - lines of unknown origin.

  4. Some examples of ‘known’ Virgo lines Mainly violin modes and calibration lines

  5. .584Hz .29-.3Hz 1.75-1.76Hz • Small disturbances ‘accumulates’ in time and clearly emerges in the total peak frequency distribution • Details not visible looking at a few days of data

  6. .585Hz .285-.3Hz 1.745-1.76-1.8Hz .2Hz 1.0Hz

  7. Harmonics of 0.333Hz: up to ~60 Hz (sidebands of 1Hz lines?) 1.0Hz: up to ~200 Hz 2.6314Hz: mainly in the ranges 315-355Hz, 560-589Hz, 602-621Hz, 805-828Hz 2.6316Hz: mainly in the range 240-290Hz 10Hz: nearly everywhere in 0-2kHz (but with decreasing persistency) 12.2782Hz 19.2309Hz: 55 harmonics spread over the whole band 38.728Hz 41.6179Hz 180.5489Hz

  8. Even after removing lines from the Virgo known line list, there are residual disturbances

  9. This happens because lines with rather small amplitude are not detected by the line monitor, but if they are persistent enough we anyway find them in the peak frequency distribution. • Then, a further cut has been applied at the level of each peakmap, but still there are small but clear residuals in the total peak map, which produce candidate excess.

  10. 124.82-128.02Hz ~1726Hz (triplet separated by .596Hz) ~1171Hz (4 lines) 615.95Hz 64.02Hz 165.4-165.78Hz 1953.17Hz 498.8Hz 1504.8-1507.6Hz 1865.4Hz 382.88Hz residuals of violin modes residuals of 1Hz and harmonics of 0.5Hz (not always present)

  11. Higher order violin modes have become visible BS 9th order? BS 11th order?

  12. Even a small peak excess can produce a large excess in the number of candidates. This is due to the fact that each disturbed frequency bin affects all the search frequency within a Doppler band range around it.

  13. Searching for non-zero spin-down candidates slightly reduces the effect of narrow disturbances Conclusions • Better cleaning by cutting bands around violin modes and calibration lines • Find lines to be cleaned on the total peak map histogram • Suggestions by commissioning/noise people very welcome

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