C. Palomba - DA Meeting 29-1- 2008 1 Update on the analysis of VSR1 data, focusing attention on the...

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C. Palomba - DA Meeting 2 9-1-2008 1 • 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 Analysis of spectral lines in VSR1 pulsar search F. Antonucci, P. Astone, S. D’Antonio, S. Frasca, C. Palomba

Transcript of C. Palomba - DA Meeting 29-1- 2008 1 Update on the analysis of VSR1 data, focusing attention on the...

Page 1: C. Palomba - DA Meeting 29-1- 2008 1 Update on the analysis of VSR1 data, focusing attention on the presence of spectral disturbances. More data  more.

C. Palomba - DA Meeting 29-1-2008

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• 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.

Analysis of spectral lines in VSR1 pulsar searchF. Antonucci, P. Astone, S. D’Antonio, S. Frasca, C. Palomba

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calibrated data

Data quality

SFDB

Average spect rum estimation

peak map

hough transf.

candidates

peak map

candidates

coincidences

coherent step

events

Data quality

SFDB

Average spect rum estimation

hough transf.

Scheme of the DA pipelineThe analysis/cleaning of the spectral disturbances is done at the level of peak maps.

We will also see how they reflect in candidate distributionThe 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

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• 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.

pers

iste

ncy

days) 2.42~ covering(each

half) by the d(interlace

28

114,11

s 576.1048

days 4.66

peakmap

FFT

FFT

obs

N

N

T

T

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Some examples of ‘known’ Virgo lines

Mainly violin modes and calibration lines

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.29-.3Hz

.584Hz

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

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.2Hz

.285-.3Hz

.585Hz

1.0Hz

1.745-1.76-1.8Hz

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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

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

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• 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.

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64.02Hz

124.82-128.02Hz

165.4-165.78Hz

382.88Hz

498.8Hz

615.95Hz

~1171Hz (4 lines)

1504.8-1507.6Hz

~1726Hz (triplet separated by .596Hz)

1865.4Hz

1953.17Hz

residuals of violin modesresiduals of 1Hz and harmonics of 0.5Hz (not always present)

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• Higher order violin modes have become visible

BS 9th order?

BS 11th order?

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0f

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• 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.

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• Searching for non-zero spin-down candidates slightly reduces the effect of narrow disturbances

sHzf

sHzf

N sd

/1017.3

/1034.8

38

9max

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• 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

Conclusions