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GRBs & VIRGO C7 run

GRBs & VIRGO C7 run. Alessandra Corsi & E. Cuoco, F. Ricci. Gamma-Ray Bursts. GRB: bursts of  -ray photons, divided in two major classes, short and long….

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GRBs & VIRGO C7 run

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  1. GRBs & VIRGO C7 run Alessandra Corsi & E. Cuoco, F. Ricci

  2. Gamma-Ray Bursts GRB: bursts of -ray photons, divided in two major classes, short and long… …followed by an “afterglow”: time decreasing (Ft-1.3) multi-wavelength emission. Optical counterpart allows host detection and redshift measurements http://imagine.gsfc.nasa.gov http://www.europhysicsnnews.com/full/12/article7/piro_fig2.jpg

  3. How is a GRB produced? Progenitor hidden from direct observation: radiation emitted at d > 1013 cm http://www.astro.psu.edu/users/niel/astro130/images/sci_american_gehrels_2002.jpg

  4. Progenitor Models Merger of compact binaries or collapse of massive rotating stars (“collapsar”):both progenitor types result in a few solar mass BH, surrounded by a torus whose accretion can provide a sudden release of energy, sufficient to power a burst. But different natural timescales imply different burst durations(short vs long  mergers vs collapsars) Thus GRBs are the result of catastrophic events:GWs should be emitted from the immediate neighborhood of the GRB central engine!

  5. How can we investigate on GRB-GW connection? …bar detectors (e.g. Explorer and Nautilus), as seen in the previous talk by G. Modestino … and Swift , 100 bursts/year, afterglows of short GRBs detected! …interferometers, e.g. VIRGO (in figure, this talk) and LIGO

  6. Estimating the strains of GWs from GRB progenitor candidates M=(m1, m2,)3/5 (m1, + m2,)-1/5 =(m1, ) q-2/5/(q+1)1/5 M=m1+m2 =m1/(1+q) • In-spiral:hc(f) ~ 1.4x10-21 (d /10 Mpc)-1M5/6 (f /100 Hz)-1/6 f  fi~ 1000 [M/2.8M ]-1 Hz • Merger: hc ~ 2.7x10-22 (d /10 Mpc)-1 (4 /M)F-1/2(a) (m /0.05)1/2 fi f  fq~ 32 kHz F(a) (M/M)-1 Em= m (4/M)2 M c2 F(a)= 1- 0.63 (1-a)3/10 • Ring-down:slowly damped mode, l=m=2, peaked at fq and width f: f ~  fq/Q(a), where Q(a)=2(1-a)-9/20 hc(fq) ~ 2.0x10-21 (d /10 Mpc) -1 (/M ) [Q /14 F]1/2 (r /0.01)1/2 Short GRBs, optimal filtering can be applied Short & Long GRBs, order of magnitude estimate of hc Kobayashi & Meszaros 2003 Short & Long GRBs Typical (optimistic?) values a ~ 0.98 m ~ 0.05 r ~ 0.01

  7. GRB 980425 d=40 Mpc GRB 980425 d=40 Mpc Characteristic GW amplitude from GRB candidate progenitors compared with the VIRGO noise curve [f Sh(f)]1/2 ns-nsm1= m2 =1.4 M - - - - in-spiral -  -  -merger bh-ns m1=12 M m2 =1.4 M - - - in-spiral -  -merger ring-down Initial Virgo Initial Virgo Advanced Virgo Advanced Virgo 62 Mpc 53 Mpc 280 Mpc 220 Mpc 2300 Mpc 1100 Mpc bh-he m1=3 M m2 =0.4 M - - - - merger ring-down Collapsar m1= m2 =1 M - - - - merger Initial Virgo Advanced Virgo Virgo Advanced Virgo 23 Mpc 27 Mpc 62 Mpc 110 Mpc 95 Mpc 490 Mpc

  8. Do we have data to analyze? http://heasarc.gsfc.nasa.gov/docs/swift/bursts/index.html …as seen in the talk by G. Modestino, 2005 events are good to be analyzed: the sample of GRBs is large are there are both long and short GRBs with redshift known and < 1 … moreover, in 2005 we have VIRGO runs C6 and C7! VIRGO C7 VIRGO C6 GRB 050915a GPS 810818575 Swift GRB 050915b GPS 810154597 Swift GRB 050916 GPS 810923765 Swift GRB 050730 GPS 806788716 Swift GRB 050801 GPS 806956095 Swift GRB 050802 GPS 807012495 Swift GRB 050803 GPS 807131655 Swift GRB 050807 GPS 810447538 HETE z unknown (no host galaxy identified), T90=53 s (15-350 keV), long GRB  search for a burst type event in VIRGO data

  9. GRB: Trigger time Select a signal region: data segment 4 minutes long, centered on the GRB trigger time (2 min before + 2 min after) Select a bkg region: long data segment around the signal region (we have taken the entire seg. 11 (in hrec),  16500 s) Run filter to search for burst-like events Run filter to search for burst-like events and remove periods coincident with vetoes Remove periods coincident with vetoes and select “good” events Define bkg statistical properties: estimate false alarm rate and set a threshold for “good” events Some “good” events are found No “good” events Estimate corresponding h Set an upper limit Using software injections, estimate the h corresponding to 90% detection efficiency for “good” events Connect with EM signal properties

  10. Background region events We are using the“Wavelet Detection Filter” (WDF) by Elena Cuoco (see VIR-NOT-EGO-1390-305 & VIR-NOT-EGO-1390-110)  16500 s

  11. Raw channel: background region event durations Distribution of events duration after clusterization grouping all successive bins (0.6 ms resolution) with S/N > 2, separated in time less than 50 ms (need to test effect of variations)

  12. Background region SNR distribution A given threshold in S/N corresponds to a false alarm rate R. The corresponding chance P to have m false alarms in a 2 min long window is: P=[(R*240)m/m!]*exp(-R*240s) The expected value of f.a.=R*240s • R=1x10-3  Expected  0.24 f.a • (S/N) thre 20 • R=4x10-3 Expected  1 f.a. • (S/N) thre 15 • R=0.1  Expected  24 f.a. •  (S/N) thre 6

  13. Raw vs hrec channel: events in the signal region Raw Signal Region: 240s long, centered on the GRB trigger time. 3x10-19 in h Need to clean the source region by performing a veto study (this may allow to lower the event rate corresponding to a given threshold and to remove events eventually found above threshold) hrec

  14. Raw vs hrec channel: signal region Raw Raw hrec hrec

  15. Work in progress • To properly set an upper-limit, filter efficiency evaluation needed  injection of known signals in the background region to test the efficiency of detection for events above the chosen threshold (still need to be done  software injections needed); • Once the filter efficiency curve is built, the h corresponding to 90% detection efficiency is used to set an upper-limit; • To improve the upper limit or remove events eventually found in the source region  Veto analysis: repeat the procedure adopted for the dark fringe in the noise channels, using the same bkg and signal regions; when coincident events are found (what does coincident mean? need to test!), use them as vetos (See also a parallel re-analysis of veto studies that is being performed by Marina Del Prete using the WDF - results reported at https://workarea.ego-gw.it/ego2/virgo/data-analysis/noise-study/veto-studies/grb/) • Finally, connect with EM signal to find clues on the progenitor: this part of the work is being developed in collaboration with the IASF-Rome/INAF: A.C. & Luigi Piro

  16. Analysis of Em_SEDBDL03: acoustic noise by picomotors in the detection bench

  17. Analysis of Em_SEDBDL03: event durations distribution

  18. Analysis of Em_SEDBDL03: use percentage vs coincidence window Preliminary Study See also the analysis by Marina Del Prete (https://workarea.ego-gw.it/ego2/virgo/data-analysis/noise-study/veto-studies/grb/)

  19. Analysis of Em_SEDBDL03: veto efficiency vs coincidence window Preliminary Study

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