GRBs GRBs & VIRGO C7 run& VIRGO C7 run

Alessandra CorsiAlessandra Corsi &&

E. Cuoco, F. RicciE. Cuoco, F. Ricci

Gamma-Ray BurstsGamma-Ray BurstsGRB: bursts of -ray photons, divided in two major classes, short and long…

…followed by an “afterglow”: time decreasing (Ft-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

How is a GRB produced?How is a GRB produced?

Progenitor hidden from direct observation: Progenitor hidden from direct observation: radiation emitted at d > 10radiation emitted at d > 101313 cm cm

http://www.astro.psu.edu/users/niel/astro130/images/sci_american_gehrels_2002.jpg

Merger of compact binaries or Merger of compact binaries or collapse of massive rotating stars collapse of massive rotating stars

(“collapsar”):both progenitor types (“collapsar”):both progenitor types result in a few solar mass BH, result in a few solar mass BH, surrounded by a torus whose surrounded by a torus whose

accretion can provide a sudden accretion can provide a sudden release of energy, sufficient to release of energy, sufficient to

power a burst. power a burst.

But different natural timescales But different natural timescales imply different burst durationsimply different burst durations

(short vs long (short vs long mergers vs mergers vs collapsars)collapsars)

Thus GRBs are the result of Thus GRBs are the result of catastrophic events:catastrophic events: GWs should GWs should be emitted from the immediate be emitted from the immediate

neighborhood of the GRB neighborhood of the GRB central engine!central engine!

Progenitor ModelsProgenitor Models

How can we investigate on GRB-GW connection?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

… … andand

Swift , Swift , 100 bursts/year, 100 bursts/year, afterglows of short GRBs afterglows of short GRBs

detected!detected!

Estimating the strains of GWs from GRB progenitor candidatesEstimating the strains of GWs from GRB progenitor candidates

In-spiral:In-spiral: hc(f) ~ 1.4x10-21 (d /10 Mpc)-1 M 5/6 (f /100 Hz)-1/6

f fi ~ 1000 [M/2.8M ]-1 Hz

Merger: 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: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)1/2Typical (optimistic?) valuesTypical (optimistic?) values

a ~ 0.98 m ~ 0.05 r ~ 0.01

M=(m1, m2,)3/5 (m1, + m2,)-1/5 = (m1, ) q-2/5/(q+1)1/5 M=m1+m2 =m1/(1+q)

Short GRBs, Short GRBs, optimal optimal

filtering can be filtering can be appliedapplied

Short & Long GRBs, Short & Long GRBs, order of magnitude order of magnitude

estimate of hestimate of hcc

Short & Long GRBs Short & Long GRBs

Kobayashi & Kobayashi & Meszaros 2003Meszaros 2003

Characteristic GW amplitude from GRB candidate progenitors compared with Characteristic GW amplitude from GRB candidate progenitors compared with the VIRGO noise curve [f Sthe VIRGO noise curve [f Shh(f)](f)]1/21/2

ns-nsns-ns mm11= m= m2 2 =1.4 M=1.4 M

- - - - in-spiral - - - - in-spiral - - -merger

bh-ns bh-ns mm11=12 M=12 M m m22 =1.4 M =1.4 M

- - - in-spiral - -merger ring-down

bh-he mbh-he m11=3 M=3 M m m22 =0.4 M =0.4 M

- - - - merger ring-down - - - - merger ring-down

Collapsar mCollapsar m11= m= m22 =1 M =1 M

- - - - merger- - - - merger

53 Mpc

220 Mpc

1100 Mpc

Advanced VirgoInitial Virgo Initial Virgo

Advanced Virgo

2300 Mpc

280 Mpc

62 Mpc

Advanced VirgoInitial Virgo

95 Mpc62 Mpc

Advanced VirgoVirgo

110 Mpc

27 Mpc 23 Mpc

490 Mpc

GRB 980425 d=40 MpcGRB 980425 d=40 MpcGRB 980425 d=40 MpcGRB 980425 d=40 Mpc

Do we have data to analyze?Do we have data to analyze?

GRB 050730 GPS 806788716 SwiftGRB 050801 GPS 806956095 SwiftGRB 050802 GPS 807012495 SwiftGRB 050803 GPS 807131655 SwiftGRB 050807 GPS 810447538 HETE

VIRGO C6VIRGO C6 GRB 050915a GPS 810818575 SwiftGRB 050915b GPS 810154597 SwiftGRB 050916 GPS 810923765 Swift

VIRGO C7VIRGO C7

z unknown (no host galaxy identified), T90=53 s (15-350 keV),

long GRB search for a burst burst type eventtype event in VIRGO data

…as seen in the talk by G. Modestino, 2005 events are good to be analyzed: the sample of GRBs is large are there there

are both long and short GRBsare both long and short GRBs with redshift known and < 1

… moreover, in 2005 we have VIRGO runs C6 and C7!

http://heasarc.gsfc.nasa.gov/docs/swift/bursts/index.html

Run filter to search for burst-like events and remove periods coincidentcoincident with vetoesvetoes

Select a signal regionsignal region: data segment 4 minutes long, centered on the GRB

trigger time (2 min before + 2 min after)

Select a bkg regionbkg region: long data segment around the signal region (we have taken the entire seg. 11 (in hrec), 16500 s)

Define bkg statistical properties: estimate false alarm rate and set

a threshold for “good” events“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 GRB: Trigger time

Background region eventsBackground region events

We are using the“Wavelet Detection Filter” We are using the“Wavelet Detection Filter” (WDF) by Elena Cuoco(WDF) by Elena Cuoco (see VIR-NOT-EGO-1390-305 & VIR-NOT-EGO-1390-110)(see VIR-NOT-EGO-1390-305 & VIR-NOT-EGO-1390-110)

16500 s

Distribution of events duration after Distribution of events duration after clusterizationclusterization grouping all successive bins (0.6 grouping all successive bins (0.6

ms resolution) with S/N > 2, separated in time less ms resolution) with S/N > 2, separated in time less than 50 ms (need to test effect of variations)than 50 ms (need to test effect of variations)

Raw channel: background region event durationsRaw channel: background region event durations

Background region SNR Background region SNR distributiondistribution

A given threshold in S/N A given threshold in S/N corresponds to a false alarm rate corresponds to a false alarm rate R. The corresponding chance P to R. The corresponding chance P to

have m false alarms in a 2 min long have m false alarms in a 2 min long window is:window is:

P=[(R*240)P=[(R*240)mm/m!]*exp(-R*240s) /m!]*exp(-R*240s)

The expected value of f.a.=The expected value of f.a.= R*240s R*240s

R=1x10R=1x10-3 -3 Expected Expected 0.24 f.a 0.24 f.a (S/N) (S/N) threthre 2020

R=4x10R=4x10-3-3 Expected Expected 1 f.a. 1 f.a. (S/N) (S/N) threthre 1515

R=0.1 R=0.1 Expected Expected 24 f.a. 24 f.a. (S/N) (S/N) threthre 66

Raw vs hrec channel: Raw vs hrec channel: events in the signal region events in the signal region

Need to clean the source Need to clean the source region by performing a region by performing a

veto study (this may veto study (this may allow to lower the event allow to lower the event rate corresponding to a rate corresponding to a given threshold and to given threshold and to

remove events eventually remove events eventually found above threshold)found above threshold)

Signal Region: 240s long, centered on the

GRB trigger time. 3x103x10-19 -19 in hin h

hrechrec

RawRaw

Raw vs hrec channel: signal regionRaw vs hrec channel: signal region

hrechrec hrechrec

RawRaw RawRaw

Work in progressWork 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 See also a parallel re-analysis of veto studies that is being performed by Marina Del PreteMarina Del Prete using the WDF - results reported at 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 this part of the work is being developed in collaboration with the IASF-Rome/INAF: A.C. the work is being developed in collaboration with the IASF-Rome/INAF: A.C. & Luigi Piro& Luigi Piro

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

Analysis of Em_SEDBDL03: event durations distributionAnalysis of Em_SEDBDL03: event durations distribution

See also the analysis by See also the analysis by Marina Del PreteMarina Del Prete ( (https://workarea.ego-gw.it/ego2/virgo/data-analysis/noise-study/veto-studies/grb/

))

Prelim

inary

Stu

dy

Analysis of Em_SEDBDL03: use percentage vs coincidence windowAnalysis of Em_SEDBDL03: use percentage vs coincidence window

Prelim

inary

Stu

dy

Analysis of Em_SEDBDL03: veto efficiency vs coincidence windowAnalysis of Em_SEDBDL03: veto efficiency vs coincidence window