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… …followed by an “afterglow”: time decreasing (F  t -1.3 ) multi-wavelength emission. Optical counterpart allows host detection and redshift measurements om/full/12/article7/piro_fig2.jpg

How is a GRB produced? Progenitor hidden from direct observation: radiation emitted at d > cm

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! Progenitor Models

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

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

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

Do we have data to analyze? GRB GPS Swift GRB GPS Swift GRB GPS Swift GRB GPS Swift GRB GPS HETE VIRGO C6 GRB a GPS Swift GRB b GPS Swift GRB GPS Swift VIRGO C7 z unknown (no host galaxy identified), T 90 =53 s ( keV), burst type event long GRB  search for a burst type event in VIRGO data there are both long and short GRBs …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! ocs/swift/bursts/index.html

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

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

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) Raw channel: background region event durations

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

Raw vs hrec channel: events in the signal region 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)  3x in h Signal Region: 240s long, centered on the GRB trigger time.  3x in h hrec Raw

Raw vs hrec channel: signal region hrec hrec Raw Raw

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; See also a parallel re-analysis of veto studies that is being performed by Marina Del Prete using the WDF - results reported at gw.it/ego2/virgo/data-analysis/noise-study/veto-studies/grb/)  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 gw.it/ego2/virgo/data-analysis/noise-study/veto-studies/grb/) gw.it/ego2/virgo/data-analysis/noise-study/veto-studies/grb/ gw.it/ego2/virgo/data-analysis/noise-study/veto-studies/grb/ this part of the work is being developed in collaboration with the IASF-Rome/INAF: A.C. & Luigi Piro  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

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

Analysis of Em_SEDBDL03: event durations distribution

See also the analysis by Marina Del Prete ( analysis/noise-study/veto-studies/grb/) analysis/noise-study/veto-studies/grb/ analysis/noise-study/veto-studies/grb/ Preliminary Study Analysis of Em_SEDBDL03: use percentage vs coincidence window

Preliminary Study Analysis of Em_SEDBDL03: veto efficiency vs coincidence window