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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 - PowerPoint PPT Presentation
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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
4
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?
12
0f
13
• 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
11
• 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