You will be presented with the full text of a science research paper. Please write an abstract for this paper. Your response should include the abstract and no additional text.

Paper text:
it is expected that stellar core collapses of supernova will radiate the burst gravitational waves , which frequency is suitable to detect with ground - based gravitational wave detectors .
the predicted burst gravitational waves have a some peculiar characteristics : type i ) big narrow spike of @xmath21 msec and following 10 - 15 msec decay ringing , type ii ) a few msec spikes iterate a few times with @xmath210 msec interval , type iii ) broad excess of @xmath25 msec with fine structure of @xmath21 msec .
the predicted burst gravitational waves have a duration and tail of signals .
since the tail structure and frequency spread is not same as popular spike noises which caused by the detector instruments , it will be possible to differentiate the gravitational waves from noises .
however , it is hard to predict the gravitational wave forms in analytically or hard to fit with a few parameters .    to identify such a signal ,
we have developed a new method by examining clustering of signal powers in time - frequency domain@xcite .
we study using tama observation data and numerical predicted gravitational waveforms by dimmelmeier at al.@xcite@xcite    in some other time - frequency domain analysis , which especially use cluster , were studied as reference @xcite , to find efficient power filter window .
our practical issue is employment much more parameters and find efficient combinations of parameters .
tama300 is a 300 m baseline laser interferometric gravitational wave detector in mitaka , tokyo , japan@xcite .
the interferometer is fabry - perot - michelson type with a power recycling technique .
the strain sensitivity of the detector is @xmath3}$ ] around 1khz , and the integral observation time is more than 3000 hours@xcite@xcite@xcite .
the typical strain sensitivity of tama detector in 9th data taking ( dt9 ) is displayed in figure 3 of the reference @xcite .
gravitational waves from stellar - core collapse of supernovae , or quasi - normal mode oscillation of black holes , has the typical time scale 10@xmath2100 msec .
it corresponds to the good sensitive observation band 100hz@xmath21khz of ground - based gravitational wave detectors as tama300 .
the time series signal from the tama interferometer is digitized by analogue - to - digital converter of 16 bit depth .
the sampling interval is 50@xmath4sec(=20khz sampling ) .
the raw data as time series signal @xmath5 is stored in frame format@xcite .
one data file consists from 20 frames , and each frame is 3.2768 sec time series signal . for the convenient of the event search analysis , we repack raw data frames into new frame of 52.4288 sec data for @xmath6 samples . for this analysis , we use part of dt9 real data which has good stableness and better sensitivity .
to investigate the search performance for burst gravitational wave , we used predicted burst gravitational waves by dimmelmeier et al.@xcite@xcite ( dfm waveforms ) .
we inject dfm waveform by software ( off - line injection ) on tama data .
since this signal @xmath5 is given after the detector response , electric servo and whitening filter , we convert dfm waveform from strain @xmath7 to @xmath5 using transfer function , and embed to raw data .
also gaussian noise simulation data is used . since real data
include many unstable spikes or glitch noises which have short time duration , these will make spurious signals of gravitational waves .
gaussian noise simulation data need to evaluate the statistical fluctuation of stable noises .      before transforming the time series data to time - frequency domain
, we remove the line noises which come from ac power line and its harmonics , mechanical resonances of suspension , etc .
the detail is given in the references@xcite@xcite .
we split time series data to 3.2 msec chunks .
time shift of next chunk is 0.8 msec , thus the neighbor chunks overlap 2.4 msec .
the figure [ fig : tchunks ] displays this process . for each of the chunks , signal powers are calculated by using fft ( short - time fourier transform , sfft ) with a hanning window .
the frequency resolution in the sffts is 312.5hz .
we dispose these sffts in time order , which is called sonogram in time - frequency domain .
one bin of data of time - frequency domain is displayed as pixel. each pixel represents the signal power at a given time and a frequency .
sfft spectrum at each frequency band is normalized using average noise .
thus the sonogram will appear as flat pixels with gaussian noise .
the figure [ fig : sfft1 ] and [ fig : sfft2 ] are examples of sonogram of tama data around spike like noise , and typical dfm waveform injected in the tama300 data .
sonogram illustrate the pixel magnitude using color .
short - fft ( sonogram ) process schematic , width=377 ]     sonogram example : tama spike like noise + spike noises spread entire the frequency band , but concentrate in short time duration.,width=453 ]     sonogram example : burst gravitational wave ( dfm waveform ) injected tama data + typical dfm waves not widely spread in frequency , but have a tail along the time axes.,width=453 ]
in time - frequency domain sonogram , we can find a closed pixels of which have higher magnitude in the figure [ fig : sfft2 ] as cluster of signal .    to identify and define a cluster are as follows :
1 ) we find all pixel which magnitude exceed signal - to - noise ratio @xmath8 .
2 ) we assume that a local maximum pixel is a peak of the cluster .
the peak specified with its time @xmath9 and frequency @xmath10 .
3 ) if one of a 8 pixels around the peak of the cluster exceed the cluster threshold , this pixel is connected pixel .
the cluster threshold is settled as square root of snr of the peak pixel .
4 ) we check the neighbor of connected pixels , again .
5 ) if no more connected pixel can not be found , we define the peak and these connected pixels as one tf - cluster. 6 ) we will iterate 2)-5 ) exclude the pixel which belongs to previous cluster , for other local peak .      when the cluster found , we calculate some parameters which shows cluster character . each cluster can be characterized by the absolute power , spread area , shape of power distribution et al .
figure [ fig : cluster_profile ] displays the schematic of projection of the cluster to each axes .
we can project the cluster by the numbers of pixels , or integrated magnitude of pixels along the orthogonal axes .
we employ following parameters to quantitate the shape of the projection ; the number of pixels in the cluster for area @xmath11 , the sum of a magnitude of pixels for cluster volume @xmath12 , @xmath13 as @xmath14-th moment of projection of area ( = the numbers of pixels ) along the time axes , @xmath15 as @xmath14-th moment of projection of magnitude along the time axes , @xmath16 as same as a @xmath13 along the frequency axes , @xmath17 as same as a @xmath15 along the frequency axes .
( @xmath18 , @xmath19 ) is a mean center of the cluster area , and ( @xmath20 , @xmath21 ) is a center of magnitude of the cluster .
higher orders @xmath13 corresponds to the standard deviation , skewness et al .
tf - cluster and its projection to each axes .
+ the pixels inside white border is recognized as one cluster.,width=377 ]
some combinations of the parameters makes possible to separate gravitational wave signal from many noises .
the figure [ fig : paramscatt1 ] and [ fig : paramscatt2 ] shows the example of two combination which efficiently separate the gaussian noise from the dfm wave forms .    in case of figure
[ fig : paramscatt1 ] ( @xmath19 - @xmath22 scatter plot ) , injected dfm wave forms concentrate within the box of blue solid line . however , many tama noises spread wider region .
most of tama noises are spike noises .
one can see that the dfm waveforms can be efficiently separated from spike noises in the figure . on the other ,
the figure [ fig : paramscatt2 ] shows the separation from gaussian noises .
gaussian noises will make spurious clusters accidentally according to the statistical fluctuations . almost of them have small size , and parameters correlation is different from spike noise of gravitational waves . in this @xmath23 - @xmath24 scatter plot , both spike noises and dfm waveforms concentrate in the blue circle .    in this study , we require all the criteria in the table [ table : criteria ] for the selection of burst gravitational wave candidates .
c l c parameters & brief meaning & criteria + & & ( unit is [ pixels ] except for @xmath10 ) + @xmath11 & area size of the cluster & @xmath25 + @xmath10 & frequency at the peak of the cluster & @xmath26}$ ] +   +   + @xmath19 & distance from center to the to the peak & @xmath27 + @xmath22 & standard deviation around @xmath19 & @xmath28 + @xmath29 & kurtosis around power weighted center @xmath21 & @xmath30 +   +   + @xmath20 & distance from weighted center to the to the peak & @xmath31 + @xmath32 & standard deviation around @xmath20 & @xmath33 +   +   + @xmath34 & & @xmath35 +   + @xmath36 & & @xmath37 +    [ default ]     scatter plot of cluster parameters @xmath19 - @xmath22 + red cross points is noises in real data .
others are injected gravitational waves ( dfm waveforms ) in simulation data.,width=377 ]     scatter plot of cluster parameters @xmath23 - @xmath24 + cluster due to gaussian noise fluctuation is spread uniformly in this figure.,width=377 ]      with the criteria in the table [ table : criteria ] , we estimate the efficiency by using monte - carlo simulation with random injection of the dfm gravitational waves in tama data . the figure fig : distvseff shows the efficiency for tf - clusters of source distance from the earth .
the efficiency depend on the type of burst waveform .
also the figure fig : hrssvseff shows the efficiency for @xmath38 : root square sum of strain amplitude . the selection criteria for clusters which peak snr@xmath13 achieved 50% average efficiency for injected dfm waveforms at 350 pc ( @xmath0 ) for type i burst@xcite , and at 170 pc for all average .
we take note that the efficiency looks higher than previous our analysis@xcite , because we set lower snr threshold for the comparison of clusters .
if we wish to derive better upper limit of event search , we must set more higher threshold of snr to reject small spurious noise .
tf - cluster selection efficiency for injected dfm waveforms ( source distance),width=377 ]     tf - cluster selection efficiency for injected dfm waveforms ( @xmath38),width=377 ]      tf - cluster selection criteria can reject typical noises efficiently in real data . using a part of tama dt9 data ( @xmath39 ) , we compare the snr histogram of peak before and after cluster parameter selection . the reduction for spurious event clusters by spike noises
are more than one order improvement for the snr@xmath1100 .
the reduction is efficient for higher snr clusters for snr@xmath1200 . because that tf - cluster method is efficient when the waveform is clear .
histogram of snr for real data + histogram of cluster snr is shown for a part of tama dt9 data .
green dashed line is all found cluster in tf domain .
red line is after the selection by criteria in the table [ table : criteria].,width=377 ]
we recently practice the newer search algorithm as tf - cluster using many cluster parameters .
the method is efficient to select gravitational event and reject noise fake , especially in larger snr case .
since the tf - cluster method is not require the exact template of wave form , it is useful to identify the burst gravitational waves which we believe only the typical characteristics is knows , or which predicted numerically without analytic waveform .    in the sonogram of this analysis
, we employed sfft , which has uniform time and frequency resolution .
however , this is not optimal time - frequency resolution in an uncertainty principle of short wave packet . according to the complemental relation between the time and the frequency
, we can improve time resolution in high frequency band , can improve frequency resolution in lower frequency band instead of time resolution .
we began to employ with wavelet transform to achieve such resolution and to make more clear appearance of burst gravitational waves .
this research is supported in part by a grant - in - aid for scientific research on priority areas ( 415 ) of the ministry of education , culture , sports , science and technology .
9 h.dimmelmeier , j.a.font and e.muller , astron .
astrophys . , 388 , 917 - 935 ( 2002 ) h.dimmelmeier , j.a.font and e.muller , astron . astrophys . , 393 , 523 - 542 ( 2002 ) r.honda , master thesis , 2007 julien sylvestre , phys .
d 66 ( 2002 ) 102004 m.ando , et al.,physical review letters , volume 86 , 3950 - 3954 , ( 2001 ) t.akutsu et al .
, physical review d 74 , 122002 ( 2006 ) m.ando , et al . , physical review d 71 , 082002 ( 2005 ) http://lappweb.in2p3.fr/virgo/framel/ tomomi akutsu , et al .
quantum grav .
23 ( 2006 ) s715
we have developed a method time - frequency ( tf ) clustering to find the burst gravitational waves for tama data analysis . 
 tf clustering method on sonogram ( spectrogram ) shows some characteristics of short duration signal . using parameters which represent the cluster shape 
 , we can efficiently identify some predicted gravitational wave forms@xcite@xcite and can exclude typical unstable spike like noises due to detector instruments . 
 the requirement of some parameters of cluster achieved roughly 50% average efficiency for injected dfm waveforms of @xmath0 for type i burst . 
 also the reduction for signal by spike noises are more than one order improvement for the snr@xmath1100 .