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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).

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virgo hierarchical search

Virgo hierarchical search

S. Frasca

Baton Rouge, March 2007

the virgo periodic source search
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)
the whole sky blind hierarchical search
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.
slide4
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

the whole sky blind hierarchical search1
The whole sky blind hierarchical search
  • The software is described in the document at http://grwavsf.roma1.infn.it/pss/docs/PSS_UG.pdf
creation of the sfts periodogram equalization and peak map construction
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)
sampled data spectrum
Sampled data spectrum
  • Periodogram of 222 (= 4194304 ) data of C7
after the comb filter
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.

slide25
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

the hough map
The Hough map
  • Now we are using the “standard” (not “adaptive”) Hough transform
  • Here are the results
slide27
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

slide28
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
slide29
1kHz band: candidates analysis

C6: frequency distribution of candidates (spin-down 0)

f [Hz]

slide30
C6: frequency distribution of candidates (spin-down 0)

f [Hz]

peaks frequency distribution

Sky distribution of candidates (~673.8Hz)

slide31
C6: frequency distribution of candidates (spin-down 0)

f [Hz]

Sky distribution of candidates (~980Hz)

peaks frequency distribution

slide32
C6: frequency distribution of candidates (spin-down 0)

f [Hz]

Sky distribution of candidates (881-889Hz)

peaks frequency distribution

slide33
C7: frequency distribution of candidates (spin-down 0)

f [Hz]

Sky distribution of candidates (779.5Hz)

peaks frequency distribution

slide35
‘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

slide36
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

slide40
‘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)
log file data crea sfdb
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”)
log file comments
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
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