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1Astronomy & Astrophysicsmanuscript no. lvemfollowup c© ESO 201119 September 2011

Implementation and testing of the first prompt search forelectromagnetic counterparts to gravitational wave trans ientsThe LIGO Scientific Collaboration and Virgo Collaboration:J. Abadie1, B. P. Abbott1, R. Abbott1, T. D. Abbott2, M. Abernathy3, T. Accadia4, F. Acernese5ac, C. Adams6,

R. Adhikari1, C. Affeldt7,8, P. Ajith1, B. Allen7,9,8, G. S. Allen10, E. Amador Ceron9, D. Amariutei11, R. S. Amin12, S. B. Anderson1, W. G. Anderson9, K. Arai1, M. A. Arain11,

M. C. Araya1, S. M. Aston13, P. Astone14a, D. Atkinson15, P. Aufmuth8,7, C. Aulbert7,8, B. E. Aylott13, S. Babak16, P. Baker17, G. Ballardin18, S. Ballmer19, D. Barker15,F. Barone5ac, B. Barr3, P. Barriga20, L. Barsotti21, M. Barsuglia22, M. A. Barton15, I. Bartos23, R. Bassiri3, M. Bastarrika3, A. Basti24ab, J. Batch15, J. Bauchrowitz7,8,

Th. S. Bauer25a, M. Bebronne4, B. Behnke16, M.G. Beker25a, A. S. Bell3, A. Belletoile4, I. Belopolski23, M. Benacquista26, J. M. Berliner15, A. Bertolini7,8, J. Betzwieser1,

N. Beveridge3, P. T. Beyersdorf27, I. A. Bilenko28, G. Billingsley1, J. Birch6, R. Biswas26, M. Bitossi24a, M. A. Bizouard29a, E. Black1, J. K. Blackburn1, L. Blackburn30,

D. Blair20, B. Bland15, M. Blom25a, O. Bock7,8, T. P. Bodiya21, C. Bogan7,8, R. Bondarescu31, F. Bondu32b, L. Bonelli24ab, R. Bonnand33, R. Bork1, M. Born7,8, V. Boschi24a,S. Bose34, L. Bosi35a, B. Bouhou22, S. Braccini24a, C. Bradaschia24a, P. R. Brady9, V. B. Braginsky28, M. Branchesi36ab, J. E. Brau37, J. Breyer7,8, T. Briant38, D. O. Bridges6,

A. Brillet32a, M. Brinkmann7,8, V. Brisson29a, M. Britzger7,8, A. F. Brooks1, D. A. Brown19, A. Brummit39, T. Bulik40bc, H. J. Bulten25ab, A. Buonanno41, J. Burguet–Castell9,

O. Burmeister7,8, D. Buskulic4, C. Buy22, R. L. Byer10, L. Cadonati42, G. Cagnoli36a, J. Cain43, E. Calloni5ab, J. B. Camp30, P. Campsie3, J. Cannizzo30, K. Cannon44,B. Canuel18, J. Cao45, C. D. Capano19, F. Carbognani18, S. Caride46, S. Caudill12, M. Cavaglia43, F. Cavalier29a, R. Cavalieri18, G. Cella24a, C. Cepeda1, E. Cesarini36b,

O. Chaibi32a, T. Chalermsongsak1, E. Chalkley13, P. Charlton47, E. Chassande-Mottin22, S. Chelkowski13, Y. Chen48, A. Chincarini49, A. Chiummo18, H. Cho50, N. Christensen51,

S. S. Y. Chua52, C. T. Y. Chung53, S. Chung20, G. Ciani11, F. Clara15, D. E. Clark10, J. Clark54, J. H. Clayton9, F. Cleva32a, E. Coccia55ab, P.-F. Cohadon38, C. N. Colacino24ab,J. Colas18, A. Colla14ab, M. Colombini14b, A. Conte14ab, R. Conte56, D. Cook15, T. R. Corbitt21, M. Cordier27, N. Cornish17, A. Corsi1, C. A. Costa12, M. Coughlin51,

J.-P. Coulon32a, P. Couvares19, D. M. Coward20, D. C. Coyne1, J. D. E. Creighton9, T. D. Creighton26, A. M. Cruise13, A. Cumming3, L. Cunningham3, E. Cuoco18, R. M. Cutler13,

K. Dahl7,8, S. L. Danilishin28, R. Dannenberg1, S. D’Antonio55a, K. Danzmann7,8, V. Dattilo18, B. Daudert1, H. Daveloza26, M. Davier29a, G. Davies54, E. J. Daw57, R. Day18,

T. Dayanga34, R. De Rosa5ab, D. DeBra10, G. Debreczeni58, J. Degallaix7,8, W. Del Pozzo25a, M. del Prete59b, T. Dent54, V. Dergachev1, R. DeRosa12, R. DeSalvo1, V. Dhillon57,S. Dhurandhar60, L. Di Fiore5a, A. Di Lieto24ab, I. Di Palma7,8, M. Di Paolo Emilio55ac, A. Di Virgilio 24a, M. Dıaz26, A. Dietz4, F. Donovan21, K. L. Dooley11, S. Dorsher61,

M. Drago59ab, R. W. P. Drever62, J. C. Driggers1, Z. Du45, J.-C. Dumas20, S. Dwyer21, T. Eberle7,8, M. Edgar3, M. Edwards54, A. Effler12, P. Ehrens1, G. Endroczi58, R. Engel1,

T. Etzel1, K. Evans3, M. Evans21, T. Evans6, M. Factourovich23, V. Fafone55ab, S. Fairhurst54, Y. Fan20, B. F. Farr63, W. Farr63, D. Fazi63, H. Fehrmann7,8, D. Feldbaum11,I. Ferrante24ab, F. Fidecaro24ab, L. S. Finn31, I. Fiori18, R. P. Fisher31, R. Flaminio33, M. Flanigan15, S. Foley21, E. Forsi6, L. A. Forte5a, N. Fotopoulos1, J.-D. Fournier32a,

J. Franc33, S. Frasca14ab, F. Frasconi24a, M. Frede7,8, M. Frei64, Z. Frei65, A. Freise13, R. Frey37, T. T. Fricke12, J. K. Fridriksson21, D. Friedrich7,8, P. Fritschel21, V. V. Frolov6,P. J. Fulda13, M. Fyffe6, M. Galimberti33, L. Gammaitoni35ab, M. R. Ganija66, J. Garcia15, J. A. Garofoli19, F. Garufi5ab, M. E. Gaspar58, G. Gemme49, R. Geng45, E. Genin18,

A. Gennai24a, L. A. Gergely67, S. Ghosh34, J. A. Giaime12,6, S. Giampanis9, K. D. Giardina6, A. Giazotto24a, C. Gill3, E. Goetz7,8, L. M. Goggin9, G. Gonzalez12,

M. L. Gorodetsky28, S. Goßler7,8, R. Gouaty4, C. Graef7,8, M. Granata22, A. Grant3, S. Gras20, C. Gray15, N. Gray3, R. J. S. Greenhalgh39, A. M. Gretarsson68, C. Greverie32a,R. Grosso26, H. Grote7,8, S. Grunewald16, G. M. Guidi36ab, C. Guido6, R. Gupta60, E. K. Gustafson1, R. Gustafson46, T. Ha69, B. Hage8,7, J. M. Hallam13, D. Hammer9,

G. Hammond3, J. Hanks15, C. Hanna1,70, J. Hanson6, J. Harms62, G. M. Harry21, I. W. Harry54, E. D. Harstad37, M. T. Hartman11, K. Haughian3, K. Hayama71, J.-F. Hayau32b,T. Hayler39, J. Heefner1, A. Heidmann38, M. C. Heintze11, H. Heitmann32, P. Hello29a, M. A. Hendry3, I. S. Heng3, A. W. Heptonstall1, V. Herrera10, M. Hewitson7,8, S. Hild3,

D. Hoak42, K. A. Hodge1, K. Holt6, J. Homan21, T. Hong48, S. Hooper20, D. J. Hosken66, J. Hough3, E. J. Howell20, B. Hughey9, S. Husa72, S. H. Huttner3, T. Huynh-Dinh6,D. R. Ingram15, R. Inta52, T. Isogai51, A. Ivanov1, K. Izumi71, M. Jacobson1, H. Jang73, P. Jaranowski40d , W. W. Johnson12, D. I. Jones74, G. Jones54, R. Jones3, L. Ju20,

P. Kalmus1, V. Kalogera63, I. Kamaretsos54, S. Kandhasamy61, G. Kang73, J. B. Kanner41, E. Katsavounidis21, W. Katzman6, H. Kaufer7,8, K. Kawabe15, S. Kawamura71,

F. Kawazoe7,8, W. Kells1, D. G. Keppel1, Z. Keresztes67, A. Khalaidovski7,8, F. Y. Khalili28, E. A. Khazanov75, B. Kim73, C. Kim76, D. Kim20, H. Kim7,8, K. Kim77, N. Kim10,

Y.-M. Kim50, P. J. King1, M. Kinsey31, D. L. Kinzel6, J. S. Kissel21, S. Klimenko11, K. Kokeyama13, V. Kondrashov1, R. Kopparapu31, S. Koranda9, W. Z. Korth1, I. Kowalska40b,D. Kozak1, V. Kringel7,8, S. Krishnamurthy63, B. Krishnan16, A. Krolak40ae, G. Kuehn7,8, R. Kumar3, P. Kwee8,7, M. Laas-Bourez20, P. K. Lam52, M. Landry15, M. Lang31,

B. Lantz10, N. Lastzka7,8, C. Lawrie3, A. Lazzarini1, P. Leaci16, C. H. Lee50, H. M. Lee78, N. Leindecker10, J. R. Leong7,8, I. Leonor37, N. Leroy29a, N. Letendre4, J. Li45,

T. G. F. Li25a, N. Liguori59ab, P. E. Lindquist1, N. A. Lockerbie79, D. Lodhia13, M. Lorenzini36a, V. Loriette29b, M. Lormand6, G. Losurdo36a, J. Luan48, M. Lubinski15, H. Luck7,8,A. P. Lundgren31, E. Macdonald3, B. Machenschalk7,8, M. MacInnis21, D. M. Macleod54, M. Mageswaran1, K. Mailand1, E. Majorana14a, I. Maksimovic29b, N. Man32a,

I. Mandel21, V. Mandic61, M. Mantovani24ac, A. Marandi10, F. Marchesoni35a, F. Marion4, S. Marka23, Z. Marka23, A. Markosyan10, E. Maros1, J. Marque18, F. Martelli36ab,

I. W. Martin3, R. M. Martin11, J. N. Marx1, K. Mason21, A. Masserot4, F. Matichard21, L. Matone23, R. A. Matzner64, N. Mavalvala21, G. Mazzolo7,8, R. McCarthy15,D. E. McClelland52, P. McDaniel21, S. C. McGuire80, G. McIntyre1, D. J. A. McKechan54, G. D. Meadors46, M. Mehmet7,8, T. Meier8,7, A. Melatos53, A. C. Melissinos81,

G. Mendell15, D. Menendez31, R. A. Mercer9, L. Merill20, S. Meshkov1, C. Messenger54, M. S. Meyer6, H. Miao20, C. Michel33, L. Milano5ab, J. Miller52, Y. Minenkov55a,

V. P. Mitrofanov28, G. Mitselmakher11, R. Mittleman21, O. Miyakawa71, B. Moe9, P. Moesta16, M. Mohan18, S. D. Mohanty26, S. R. P. Mohapatra42, D. Moraru15, G. Moreno15,

N. Morgado33, A. Morgia55ab, T. Mori71, S. Mosca5ab, K. Mossavi7,8, B. Mours4, C. M. Mow–Lowry52, C. L. Mueller11, G. Mueller11, S. Mukherjee26, A. Mullavey52,H. Muller-Ebhardt7,8, J. Munch66, P. G. Murray3, A. Mytidis11, T. Nash1, L. Naticchioni14ab, R. Nawrodt3, V. Necula11, J. Nelson3, G. Newton3, E. Nishida71, A. Nishizawa71,

F. Nocera18, D. Nolting6, L. Nuttall54, E. Ochsner41, J. O’Dell39, E. Oelker21, G. H. Ogin1, J. J. Oh69, S. H. Oh69, R. G. Oldenburg9, B. O’Reilly6, R. O’Shaughnessy9,

C. Osthelder1, C. D. Ott48, D. J. Ottaway66, R. S. Ottens11, H. Overmier6, B. J. Owen31, A. Page13, G. Pagliaroli55ac, L. Palladino55ac, C. Palomba14a, Y. Pan41, C. Pankow11,F. Paoletti24a,18, M. A. Papa16,9, M. Parisi5ab, A. Pasqualetti18, R. Passaquieti24ab, D. Passuello24a, P. Patel1, D. Pathak54, M. Pedraza1, P. Peiris82, L. Pekowsky19, S. Penn83,

C. Peralta16, A. Perreca19, G. Persichetti5ab, M. Phelps1, M. Pickenpack7,8, F. Piergiovanni36ab, M. Pietka40d , L. Pinard33, I. M. Pinto84, M. Pitkin3, H. J. Pletsch7,8, M. V. Plissi3,

R. Poggiani24ab, J. Pold7,8, F. Postiglione56, M. Prato49, V. Predoi54, L. R. Price1, M. Prijatelj7,8, M. Principe84, S. Privitera1, R. Prix7,8, G. A. Prodi59ab, L. Prokhorov28,O. Puncken7,8, M. Punturo35a, P. Puppo14a, V. Quetschke26, F. J. Raab15, D. S. Rabeling25ab, I. Racz58, H. Radkins15, P. Raffai65, M. Rakhmanov26, C. R. Ramet6, B. Rankins43,

P. Rapagnani14ab, S. Rapoport52,92, V. Raymond63, V. Re55ab, K. Redwine23, C. M. Reed15, T. Reed85, T. Regimbau32a, S. Reid3, D. H. Reitze11, F. Ricci14ab, R. Riesen6,

K. Riles46, N. A. Robertson1,3, F. Robinet29a, C. Robinson54, E. L. Robinson16, A. Rocchi55a, S. Roddy6, C. Rodriguez63, M. Rodruck15, L. Rolland4, J. Rollins23, J. D. Romano26,

R. Romano5ac, J. H. Romie6, D. Rosinska40c f , C. Rover7,8, S. Rowan3, A. Rudiger7,8, P. Ruggi18, K. Ryan15, H. Ryll7,8, P. Sainathan11, M. Sakosky15, F. Salemi7,8, L. Sammut53,L. Sancho de la Jordana72, V. Sandberg15, S. Sankar21, V. Sannibale1, L. Santamarıa1, I. Santiago-Prieto3, G. Santostasi86, B. Sassolas33, B. S. Sathyaprakash54, S. Sato71,

M. Satterthwaite52, P. R. Saulson19, R. L. Savage15, R. Schilling7,8, S. Schlamminger87, R. Schnabel7,8, R. M. S. Schofield37, B. Schulz7,8, B. F. Schutz16,54, P. Schwinberg15,

J. Scott3, S. M. Scott52, A. C. Searle1, F. Seifert1, D. Sellers6, A. S. Sengupta1, D. Sentenac18, A. Sergeev75, D. A. Shaddock52, M. Shaltev7,8, B. Shapiro21, P. Shawhan41,D. H. Shoemaker21, A. Sibley6, X. Siemens9, D. Sigg15, A. Singer1, L. Singer1, A. M. Sintes72, G. Skelton9, B. J. J. Slagmolen52, J. Slutsky12, J. R. Smith2, M. R. Smith1,

N. D. Smith21, R. J. E. Smith13, K. Somiya48, B. Sorazu3, J. Soto21, F. C. Speirits3, L. Sperandio55ab, M. Stefszky52, A. J. Stein21, E. Steinert15, J. Steinlechner7,8,

S. Steinlechner7,8, S. Steplewski34, A. Stochino1, R. Stone26, K. A. Strain3, S. Strigin28, A. S. Stroeer26, R. Sturani36ab, A. L. Stuver6, T. Z. Summerscales88, M. Sung12,S. Susmithan20, P. J. Sutton54, B. Swinkels18, M. Tacca18, L. Taffarello59c, D. Talukder34, D. B. Tanner11, S. P. Tarabrin7,8, J. R. Taylor7,8, R. Taylor1, P. Thomas15, K. A. Thorne6,

K. S. Thorne48, E. Thrane61, A. Thuring8,7, C. Titsler31, K. V. Tokmakov79, A. Toncelli24ab, M. Tonelli24ab, O. Torre24ac, C. Torres6, C. I. Torrie1,3, E. Tournefier4, F. Travasso35ab,

G. Traylor6, M. Trias72, K. Tseng10, L. Turner1, D. Ugolini89, K. Urbanek10, H. Vahlbruch8,7, G. Vajente24ab, M. Vallisneri48, J. F. J. van den Brand25ab, C. Van Den Broeck25a,

S. van der Putten25a, A. A. van Veggel3, S. Vass1, M. Vasuth58, R. Vaulin21, M. Vavoulidis29a, A. Vecchio13, G. Vedovato59c, J. Veitch54, P. J. Veitch66, C. Veltkamp7,8,D. Verkindt4, F. Vetrano36ab, A. Vicere36ab, A. E. Villar1, J.-Y. Vinet32a, S. Vitale68, S. Vitale25a, H. Vocca35a, C. Vorvick15, S. P. Vyatchanin28, A. Wade52, S. J. Waldman21,

L. Wallace1, Y. Wan45, X. Wang45, Z. Wang45, A. Wanner7,8, R. L. Ward22, M. Was29a, P. Wei19, M. Weinert7,8, A. J. Weinstein1, R. Weiss21, L. Wen48,20, S. Wen6, P. Wessels7,8,

M. West19, T. Westphal7,8, K. Wette7,8, J. T. Whelan82, S. E. Whitcomb1,20, D. White57, B. F. Whiting11, C. Wilkinson15, P. A. Willems1, H. R. Williams31, L. Williams11,B. Willke7,8, L. Winkelmann7,8, W. Winkler7,8, C. C. Wipf21, A. G. Wiseman9, H. Wittel7,8, G. Woan3, R. Wooley6, J. Worden15, J. Yablon63, I. Yakushin6, H. Yamamoto1,

K. Yamamoto7,8, H. Yang48, D. Yeaton-Massey1, S. Yoshida90, P. Yu9, M. Yvert4, A. Zadrozny40e,91, M. Zanolin68, J.-P. Zendri59c, F. Zhang45, L. Zhang1, W. Zhang45,

Z. Zhang20, C. Zhao20, N. Zotov85, M. E. Zucker21, J. Zweizig1,

M. Boer93, R. Fender74, N. Gehrels30, A. Klotz94, E. O. Ofek62,95, M. Smith31, M. Sokolowski91, B. W. Stappers96, I. Steele97, J. Swinbank98, and R. A. M. J. Wijers98

(Affiliations can be found after the references)

https://ntrs.nasa.gov/search.jsp?R=20120008346 2020-07-16T03:24:33+00:00Z

ABSTRACT

Aims. A transient astrophysical event observed in both gravitational wave (GW) and electromagnetic (EM) channels would yieldrich scientific rewards. A first program initiating EM follow-ups to possible transient GW events has been developed and exercisedby the LIGO and Virgo community in association with several partners. In this paper, we describe and evaluate the methodsused topromptly identify and localize GW event candidates and to request images of targeted sky locations.Methods. During two observing periods (Dec 17 2009 to Jan 8 2010 and Sep2 to Oct 20 2010), a low-latency analysis pipeline wasused to identify GW event candidates and to reconstruct mapsof possible sky locations. A catalog of nearby galaxies and Milky Wayglobular clusters was used to select the most promising sky positions to be imaged, and this directional information wasdelivered toEM observatories with time lags of about thirty minutes. A Monte Carlo simulation has been used to evaluate the low-latency GWpipeline’s ability to reconstruct source positions correctly.Results. For signals near the detection threshold, our low-latency algorithms often localized simulated GW burst signals to tensof square degrees, while neutron star/neutron star inspirals and neutron star/black hole inspirals were localized to a few hundredsquare degrees. Localization precision improves for moderately stronger signals. The correct sky location of signalswell abovethreshold and originating from nearby galaxies may be observed with∼50% or better probability with a few pointings of wide-fieldtelescopes.

Key words. gravitational waves - methods: observational

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 3

1. Introduction

The Laser Interferometer Gravitational-Wave Observatory(LIGO) (Abbott et al. 2009a) and Virgo (Accadia et al. 2011)have taken significant steps toward gravitational wave (GW)astronomy over the past decade. The LIGO ScientificCollaboration operates two LIGO observatories in the U.S.along with the GEO 600 detector (Luck et al. 2010) in Germany.Together with Virgo, located in Italy, they form a detector net-work capable of detecting GW signals arriving from all direc-tions. Their most recent joint data taking run was between July2009 and October 2010. GEO 600 and Virgo are currently oper-ating during summer 2011, while the LIGO interferometers havebeen decommissioned for the upgrade to the next-generationAdvanced LIGO detectors (Harry et al. 2010), expected to beoperational around 2015. Virgo will also be upgraded to be-come Advanced Virgo (Acernese et al. 2008). Additionally, thenew LCGT detector (Kuroda & The LCGT Collaboration 2010)is planned in Japan. These “advanced era” detectors are expectedto detect compact binary coalescences, possibly at a rate ofdozens per year, after reaching design sensitivity (Abadieet al.2010b).

Detectable, transient GW signals in the LIGO/Virgo fre-quency band require bulk motion of mass on short time scales.Emission in other channels is also possible in many such rapidlychanging massive systems. This leads to the prospect that sometransient GW sources may have corresponding electromagnetic(EM) counterparts which could be discovered with a low latencyresponse to GW triggers (Sylvestre 2003; Kanner et al. 2008;Stubbs 2008; Kulkarni & Kasliwal 2009; Bloom et al. 2009).

Finding these EM counterparts would yield rich scientificrewards (see Sect. 2), but is technically challenging due toim-perfect localization of the gravitational wave signal and uncer-tainty regarding the relative timing of the GW and EM emis-sions. This paper details our recent effort to construct a promptsearch for joint GW/EM sources between the LIGO/Virgo de-tector network and partner EM observatories (see Sect. 3). Thesearch was demonstrated during two periods of live LIGO/Virgorunning: the “winter” observing period in December 2009 andJanuary 2010 and the “autumn” observing period in Septemberand October 2010. We focus here on the design and performanceof software developed for rapid EM follow-ups of GW candi-date events, as well as the procedures used to identify significantGW triggers and to communicate the most likely sky locationsto partner EM observatories. The analysis of the observationaldata is in progress, and will be the subject of future publications.

2. Motivation

2.1. Sources

A variety of EM emission mechanisms, both observed and the-oretical, may occur in association with observable GW sources.Characteristics of a few scenarios helped inform the designandexecution of this search. Here, some likely models are presented,along with characteristics of the associated EM emission.

2.1.1. Compact Binary Coalescence

Compact binary systems consisting of neutron stars and/or blackholes are thought to be the most common sources of GW emis-sion detectable with ground-based interferometers. Radiation ofenergy and angular momentum causes the orbit to decay (in-spiral) until the objects merge (Cutler et al. 1993). For a system

consisting of two neutron stars (NS-NS) or a neutron star anda stellar-mass black hole (NS-BH), the inspiral stage producesthe most readily detectable GW signal. The energy flux reach-ing Earth depends on the inclination angle of the binary orbitrelative to the line of sight. The initial LIGO-Virgo network issensitive to optimally oriented NS-NS mergers from as far awayas 30 Mpc, and mergers between a NS and a 10M⊙ black holeout to 70 Mpc (Abadie et al. 2010b). With advanced detectors,these range limits are expected to increase to 440 and 930 Mpc,respectively.

The energetics of these systems suggest that an EM counter-part is also likely. The final plunge radiates of order 1053 ergsof gravitational binding energy as gravitational waves. Ifeven asmall fraction of this energy escapes as photons in the observingband, the resulting counterpart could be observable to large dis-tances. The EM transients that are likely to follow a NS-BH orNS-NS merger are described below.

Short-hard gamma-ray bursts (SGRBs), which typically havedurations of 2 seconds or less, may be powered by NS-NSor NS-BH mergers (Nakar 2007; Meszaros 2006; Piran 2004).Afterglows of SGRBs have been observed in wavelengthsfrom radio to X-ray, and out to Gpc distances (Nakar 2007;Gehrels et al. 2009). Optical afterglows have been observedfrom a few tens of seconds to a few days after the GRB trigger(see, for example, Klotz et al. (2009a)), and fade with a powerlaw t−α, whereα is between 1 and 1.5. At 1 day after the triggertime, the apparent optical magnitude would be between 12 and20 for a source at 50 Mpc (Kann et al. 2011), comparable to thedistance to which LIGO and Virgo could detect the merger.

Even if a compact binary coalescence is not observable ingamma-rays, there is reason to expect it will produce an observ-able optical counterpart. Li & Paczynski (1998) suggestedthat,during a NS-NS or NS-BH merger, some of the neutron star’smass is tidally ejected. In their model, the ejected neutron-richmatter produces heavy elements throughr-process nucleosyn-thesis, which subsequently decay and heat the ejecta, poweringan optical afterglow known as a kilonova. The predicted opticalemission is roughly isotropic, and so is observable regardless ofthe orientation of the original binary system. This emission isexpected to peak after about one day, around magnitude 18 fora source at 50 Mpc (Metzger et al. 2010), and then fade over thecourse of a few days following the merger.

2.1.2. Stellar Core Collapse

Beyond the compact object mergers described above, some otherastrophysical processes are plausible sources of observable GWemission. GW transients with unknown waveforms may be dis-covered by searching the LIGO and Virgo data for short periodsof excess power (bursts). The EM counterparts to some likelysources of GW burst signals are described below.

Core-collapse supernovae are likely to produce some amountof gravitational radiation, though large uncertainties still existin the expected waveforms and energetics. Most models predictGW spectra that would be observable by initial LIGO and Virgofrom distances within some fraction of the Milky Way, but notfrom the Mpc distances needed to observe GWs from anothergalaxy (Ott 2009). Neutrino detectors such as SuperKamiokandeand IceCube should also detect a large number of neutrinos froma Galactic supernova (Beacom & Vogel 1999; Halzen & Raffelt2009; Leonor et al. 2010). Galactic supernovae normally wouldbe very bright in the optical band, but could be less than obviousif obscured by dust or behind the Galactic center. Optical emis-sion would first appear hours after the GW and neutrino signal

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 4

and would peak days to weeks later, fading over the course ofweeks or months.

Long-soft gamma-ray bursts (LGRBs) are believed to be as-sociated with the core collapse of massive stars (Woosley 1993;MacFadyen & Woosley 1999; Piran 2004; Woosley & Bloom2006; Metzger et al. 2011). A large variety of possible GWemitting mechanisms within these systems have been pro-posed, with some models predicting GW spectra that wouldbe observable from distances of a few Mpc with initial LIGOand Virgo (Fryer et al. 2002; Kobayashi & Meszaros 2003a;Corsi & Meszaros 2009; Piro & Pfahl 2007; Korobkin et al.2011; Kiuchi et al. 2011). The afterglows of LGRBs, like theafterglows of SGRBs, typically show power law fading withα = 1 − 1.5. However, the peak isotropic equivalent luminos-ity of LGRB afterglows is typically a factor of 10 brighter thanSGRB afterglows (Nakar 2007; Kann et al. 2010).

An off-axis or sub-energetic LGRB may also be observed asan orphan afterglow or dirty fireball (Granot et al. 2002; Rhoads2003). These transients brighten over the course of severaldaysor even weeks, depending on the observing band and viewing an-gle. Identifying orphan afterglows in large area surveys, such asRykoff et al. (2005), has proven difficult, but a GW trigger mayhelp distinguish orphan afterglows from other EM variability.

2.1.3. Other Possible Sources

Cosmic string cusps are another possible joint source ofGW (Siemens et al. 2006; Damour & Vilenkin 2000) and EM(Vachaspati 2008) radiation. If present, their distinct GWsig-nature will distinguish them from other sources. On the otherhand, even unmodeled GW emissions can be detected using GWburst search algorithms, and such events may in some cases pro-duce EM radiation either through internal dynamics or throughinteraction with the surrounding medium. Thus, our joint searchmethods should allow for a wide range of possible sources.

2.2. Investigations enabled by joint GW/EM observations

A variety of astrophysical information could potentially be ex-tracted from a joint GW/EM signal. In understanding the pro-genitor physics, the EM and GW signals are essentially com-plementary. The GW time series directly traces the bulk motionof mass in the source, whereas EM emissions arising from out-flows or their interaction with the interstellar medium giveonlyindirect information requiring inference and modeling. Ontheother hand, observing an EM counterpart to a GW signal reducesthe uncertainty in the source position from degrees to arcsec-onds. This precise directional information can lead to identifica-tion of a host galaxy, and a measurement of redshift. Some spe-cific questions that may be addressed with a collection of jointGW/EM signals are discussed below.

If the GW source is identified as a NS-NS or NS-BH merger,additional investigations are enabled with an EM counterpart.The observation of the EM signal will improve the estimationof astrophysical source parameters. For example, when attempt-ing parameter estimation with a bank of templates and a sin-gle data stream, the source’s distance, inclination angle,andangular position are largely degenerate. A precise source po-sition from an EM counterpart would help break this degener-acy (Dalal et al. 2006; Nissanke et al. 2010). High precisionpa-rameter estimation may even constrain the NS equation of state(Cutler et al. 1993; Vallisneri 2000; Flanagan & Hinderer 2008;

Andersson et al. 2011; Pannarale et al. 2011; Hinderer et al.2010).

Observing EM counterparts of NS-NS and NS-BH mergerevents will give strong evidence as to which class of source,ifeither, is the source of SGRBs (Bloom et al. 2009). In addition,if some neutron star mergers are the sources of SGRBs, a collec-tion of joint EM/GW observations would allow an estimate ofthe SGRB jet opening angle by comparing the number of mergerevents with and without observable prompt EM emission, andsome information would be obtainable even from a single loudevent (Kobayashi & Meszaros 2003b; Seto 2007).

An ensemble of these observations could provide a novelmeasurement of cosmological parameters. Analysis of the well-modeled GW signal will provide a measurement of the lumi-nosity distance to the source, while the redshift distance is mea-surable from the EM data. Taken together, they provide a di-rect measurement of the local Hubble constant (Schutz 1986;Markovic 1993; Dalal et al. 2006; Nissanke et al. 2010).

Finally, all of the above assume that general relativity is thecorrect theory of gravity on macroscopic scales. Joint EM/GWobservations can also be used to test certain predictions ofgen-eral relativity, such as the propagation speed and polarizations ofGWs. (Will 2005; Yunes et al. 2010; Kahya 2011).

In the case that the transient GW source is not a binarymerger event, the combination of GW and EM information willagain prove very valuable. In this scenario, the gravitationalwaveform will not be known a priori. Any distance estimatewould be derived from the EM data, which would then set theoverall scale for the energy released as GWs.

As in the merger case, the linking of a GW signal with aknown EM phenomenon will provide insight into the underly-ing physical process. For example, the details of the central en-gine that drives LGRBs are unknown. The GW signal could givecrucial clues to the motion of matter in the source, and poten-tially distinguish between competing models. A similar insightinto the source mechanism could be achieved for an observa-tion of GW emission associated with a supernova. Rapid identi-fication may also allow observation of a supernova in its ear-liest moments, an opportunity that currently depends on luck(Soderberg et al. 2008).

2.3. Extend GW Detector Reach

Finding an EM counterpart associated with a LIGO/Virgo triggerwould increase confidence that a truly astrophysical event hadbeen observed in the GW data. Using EM transients to help dis-tinguish low amplitude GW signals from noise events allows alowering of the detection threshold, as was done in searchessuchas Abbott et al. (2010). Kochanek & Piran (1993) estimated thatthe detectable amplitude could be reduced by as much as a fac-tor of 1.5, increasing the effective detector horizon distance (themaximum distance at which an optimally oriented and locatedsystem could be detected) by the same factor and thus increas-ing the detection rate by a factor of 3. In practice, the actual im-provement in GW sensitivity achieved by pairing EM and GWobservations depends on many factors unique to each search,in-cluding details of the source model and data set, and so is diffi-cult to predict in advance.

In the case of a coincidence between a GW signal and a dis-covered EM transient, the joint significance may be calculatedby assuming that the backgrounds of the EM and GW search areindependent. The False Alarm Rate (FAR) of a GW/EM coin-cidence is the FAR of the GW signal, as described in Sect. 4.2,timesα, the expected fraction of observations associated with a

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 5

false or unrelated EM transient. The false alarm fractionα maybe estimated using fields from surveys not associated with GWtriggers. The value ofα will depend heavily on the telescope be-ing used, the cuts selected in image analysis, galactic latitude ofthe source and other factors.

To use EM transience in this way, the time-domain sky inthe wavelength of interest must be well understood. Transientsthat are found in directional and time coincidence with GW trig-gers would increase confidence in the GW signal only if thechance of a similar, incidental coincidence is understood to below (Kulkarni & Kasliwal 2009).

2.4. Implications for Search Design

Characteristics of the target sources helped determine when andwhere to seek the EM counterparts to GW event candidates. Forreasons discussed in this section, the search strategy presented inthis paper emphasizes capturing images as soon as possible afterthe GW trigger, along with follow-up images over subsequentnights. Some of the most probable models are extragalactic,andso field selection was strongly weighted towards regions con-taining nearby galaxies.

The observations and theoretical models of EM transientsdiscussed above provided guidance when choosing the observ-ing cadence. GRB optical afterglows have been observed dur-ing the prompt emission phase (Klotz et al. 2009b) and up tomany hours after the trigger. For this search, the first attempt toimage the source position was made as soon as possible aftervalidating a GW trigger. In both the kilonova (Li & Paczynski1998) and supernova (Ott 2009) models, some time lag existsbetween the release of GW and EM emission, primarily due tothe time it takes the outflowing material to become opticallythin.This time lag may be from several hours for a kilonova, up todays for a core-collapse supernova. Furthermore, Coward etal.(2011) show that for GRBs that are off-axis, the optical afterglowmay not be visible until days after the burst. For these reasons,repeated observations over several nights are desirable. Also, thelight curves obtained by observing the same fields over multiplenights are critical clues for discovering and classifying transientsources.

Knowing where to look for the counterpart to a GW trig-ger is challenging. Directional estimates of low signal to noiseratio (SNR) binary inspiral sources with the 2009–10 GW detec-tor network have uncertainties of several tens of square degrees(Fairhurst 2009). This suggests using telescopes with a field ofview (FOV) of at least a few square degrees if possible. Evenwith such a “wide field” instrument, there is a striking mismatchbetween the large area needing to be searched, and the size ofasingle FOV.

The problem may be partially mitigated by making use ofthe known mass distribution in the nearby universe. A searchforGW counterparts can dramatically reduce the needed sky cov-erage by focusing observations on galaxies within the distancelimits of the GW detectors (Kanner et al. 2008; Nuttall & Sutton2010). Limiting the search area to known galaxies may alsoimprove the feasibility of identifying the true counterpartfrom among other objects with time-varying EM emissions(Kulkarni & Kasliwal 2009). Even within the Milky Way, asearch may emphasize known targets by seeking counterpartswithin globular clusters, where binary systems of compact ob-jects may form efficiently (O’Leary et al. 2007).

An emphasis on extragalactic and globular-cluster sourceshas the potential drawback that any counterparts in the plane ofthe Milky Way may be missed. Also, neutron star mergers that

occur at large distances from their host galaxies may not be ob-served, though the population with large kicks should be small(Berger 2010; Kelley et al. 2010).

Our selection of fields to observe was weighted towards ar-eas containing known galaxies within 50 Mpc. The utilized cata-log of nearby galaxies and globular clusters, and the process forselecting fields to observe, is described in Sect. 5.

3. GW and EM Instruments

3.1. Gravitational Wave Detector Network

The LIGO and Virgo detectors are based on Michelson-typeinterferometers, with Fabry-Perot cavities in each arm andapower recycling mirror between the laser and beamsplitter todramatically increase the power in the arms relative to a simpleMichelson design. The GEO 600 detector uses a folded interfer-ometer without Fabry-Perot arm cavities but with an additionalrecycling mirror at the output to resonantly enhance the GW sig-nal. As a gravitational wave passes through each interferometer,it induces a “strain” (a minuscule change in length on the orderof 1 part in 1021 or less) on each arm of the interferometer dueto the quadrupolar perturbation of the spacetime metric. The in-terferometers are designed to measure thedifferential strain onthe two arms through interference of the laser light when thetwo beams are recombined at the beam splitter, with the rela-tive optical phase modulated by the passing gravitational wave(Abbott et al. 2009a).

In 2009–2010 there were two operating LIGO interfer-ometers, each with 4-km arms: H1, located near Hanford,Washington, and L1, located in Livingston Parish, Louisiana.1

Virgo (V1) has arms of length 3 km and is located near Cascina,Italy. GEO 600 data was not used in the online search describedin this paper, but was available for offline reanalysis of promis-ing event candidates. The large physical separation between theinstruments means that the effects of local environmental back-ground can be mitigated by requiring a coincident signal in mul-tiple interferometers. Each interferometer is most sensitive toGW signals traveling parallel or anti-parallel to zenith, but theantenna pattern varies gradually over the sky, so that the detec-tors are essentially all-sky monitors.

The EM follow-up program described in this paper was exer-cised during the 2009–2010 science runs. While single-detectortriggers had been generated with low latency in earlier scienceruns for diagnostic and prototyping purposes, 2009–2010 wasthe first time that a systematic search for GW transients usingthe full LIGO-Virgo network was performed with low latency,and the first time that alerts were sent to external observatories.

3.2. Optical and Other Electromagnetic Observatories

In an effort to explore various approaches, the telescope networkused in 2009–10 was intentionally heterogeneous. However,most of instruments had large fields of view to accommodatethe imprecise GW position estimate. The approximate locationof each EM observatory in shown in Fig. 1.

1 Earlier science runs included a second interferometer at Hanford,called H2, with 2-km arms. H2 will reappear as part of Advanced LIGO,either as a second 4-km interferometer at Hanford or else at asite inWestern Australia. The latter option would greatly improvethe sourcelocalization capabilities of the network (Fairhurst 2011;Schutz 2011).

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 6

3.2.1. Optical Instruments

The Palomar Transient Factory (PTF) (Law et al. 2009;Rau et al. 2009) operates a 7.3 square degree FOV cameramounted on the 1.2 m Oschin Telescope at Palomar Observatory.A typical 60 s exposure detects objects with a limiting magni-tudeR = 20.5. For the autumn observing period, the PTF teamdevoted ten fields over several nights at a target rate of 1 triggerfor every three weeks.

Pi of the Sky (Malek et al. 2009) observed using a camerawith a 400 square degree FOV and exposures to limiting mag-nitude 11–12. It was located in Koczargi Stare, near Warsaw.The camera was a prototype for a planned system that will si-multaneously image two steradians of sky. The target rate wasapproximately 1 per week in the autumn run, followed up withhundreds of 10 s exposures over several nights.

TheQUEST camera (Baltay et al. 2007), currently mountedon the 1 m ESO Schmidt Telescope at La Silla Observatory,views 9.4 square degrees of sky in each exposure. The telescopeis capable of viewing to a limiting magnitude ofR ∼ 20. TheQUEST team devoted twelve 60 s exposures over several nightsfor each trigger in both the winter and autumn periods, with atarget rate of 1 trigger per week.

ROTSE III (Akerlof et al. 2003) is a collection of four robotictelescopes spread around the world, each with a 0.45 m apertureand 3.4 square degree FOV. No filters are used, so the spectralresponse is that of the CCDs, spanning roughly 400 to 900 nm.The equivalentR band limiting magnitude is about 17 in a 20 sexposure. The ROTSE team arranged for a series of thirty imagesfor the first night, and several images on following nights, foreach autumn run trigger, with a target rate of 1 trigger per week.

SkyMapper (Keller et al. 2007) is a survey telescope locatedat Siding Spring observatory in Australia. The mosaic cameracovers 5.7 square degrees of sky in each field, and is mountedon a 1.35 m telescope with a collecting area equivalent to anunobscured 1.01 m aperture. It is designed to reach a limitingmagnitudeg ∼ 21 (>7 sigma) in a 110 s exposure. SkyMapperaccepted triggers in the autumn run with a target rate of 1 perweek, with several fields collected for each trigger.

TAROT (Klotz et al. 2009a) operates two robotic 25 cm tele-scopes, one at La Silla in Chile and one in Calern, France. Likethe ROTSE III system, each TAROT instrument has a 3.4 squaredegree FOV. A 180 second image with TAROT in ideal condi-tions has a limitingR magnitude of 17.5. During the winter run,TAROT observed a single field during one night for each trig-ger. In the autumn run, the field selected for each trigger wasobserved over several nights. TAROT accepted triggers withatarget rate of 1 per week.

Zadko Telescope (Coward et al. 2010) is a 1 m telescope lo-cated in Western Australia. The current CCD imager observesfields of 0.15 square degrees down to magnitude∼ 20 in theR band for a typical 180 s exposure. For each accepted triggerin the autumn run, Zadko repeatedly observed the five galaxiesconsidered most likely to host the source over several nights. Thetarget trigger rate for Zadko was one trigger per week.

The Liverpool telescope (Steele et al. 2004) is a 2 mrobotic telescope situated at the Observatorio del Roque deLosMuchachos on La Palma. For this project the RATCam instru-ment, with a 21 square arcminute field of view, was used. Thisinstrumentation allows a five minute exposure to reach magni-tude r′ = 21. This project was awarded 8 hours of target-of-opportunity time, which was split into 8 observations of 1 houreach, with a target rate of 1 trigger per week.

Fig. 1. A map showing the approximate positions of telescopesthat participated in the project. The Swift satellite observatory isnoted at an arbitrary location. The image is adapted from a blankworld map placed in the public domain by P. Dlouhy.

3.2.2. Radio and X-ray Instruments

LOFAR (Fender et al. 2006; de Vos et al. 2009; Stappers et al.2011) is a dipole array radio telescope based in the Netherlandsbut with stations across Europe. The array is sensitive to fre-quencies in the range of 30 to 80 MHz and 110 to 240 MHz, andcan observe multiple simultaneous beams, each with a FWHMvarying with frequency up to a maximum of around 23o. Duringthe autumn run, LOFAR accepted triggers at a target rate of 1per week and followed up each with a four-hour observation inits higher frequency band, providing a∼25 square degree fieldof view.

Although not used in the prompt search during the sciencerun, the Expanded Very Large Array (Perley et al. 2011) wasused to follow up a few triggers after the run with latencies of3 and 5 weeks.

TheSwift satellite (Gehrels et al. 2004) carries three instru-ments, each in different bands. Swift granted several target ofopportunity observations with two of these, the X-ray Telescope(XRT) and UV/Optical Telescope (UVOT), for the winter andautumn observing periods. The XRT is an imaging instrumentwith a 0.15 square degree FOV, sensitive to fluxes around 10−13

ergs/cm2/s in the 0.5-10 keV band. A few fields were imaged foreach trigger that Swift accepted.

4. Trigger Selection

The online analysis process which produced GW candidate trig-gers to be sent to telescopes is outlined in Fig. 2. After dataandinformation on data quality were copied from the interferome-ter sites to computing centers, three different data analysis algo-rithms identified triggers and determined probability skymaps.The process of downselecting this large collection of triggers tothe few event candidates that received EM follow-up is describedin this section.

After event candidates were placed in a central archive, addi-tional software used the locations of nearby galaxies and MilkyWay globular clusters to select likely source positions (Sect. 5).Triggers were manually vetted, then the selected targets werepassed to partner observatories which imaged the sky in an at-tempt to find an associated EM transient. Studies demonstratingthe performance of this pipeline on simulated GWs are presentedin Sect. 6.

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 7

Fig. 2. A simplified flowchart of the online analysis with approx-imate time requirements for each stage. Data and informationon data quality were generated at the Hanford, Livingston, andVirgo interferometers (H1, L1, and V1) and copied to central-ized computer centers. The online event trigger generatorspro-duced coincident triggers which were written into the GraCEDbarchive. The LUMIN and GEM algorithms selected statisticallysignificant triggers from the archive and chose pointing loca-tions. Significant triggers generated alerts, and were validatedmanually. If no obvious problem was found, the trigger’s esti-mated coordinates were sent to telescopes for potential follow-up.

4.1. Trigger Generation

Sending GW triggers to observatories with less than an hour la-tency represents a major shift from past LIGO/Virgo data anal-yses, which were reported outside the collaboration at soonestseveral months after the data collection. Reconstructing sourcepositions requires combining the data streams from the LIGO-Virgo network using either fully coherent analysis or a coinci-dence analysis of single-detector trigger times. A key stepin la-tency reduction was the rapid data replication process, in whichdata from all three GW observatory sites were copied to severalcomputing centers within a minute of collection.

For the EM follow-up program, three independent GW de-tection algorithms (trigger generators), ran promptly as databecame available, generating candidate triggers with latenciesbetween five and eight minutes. Omega Pipeline and coherentWaveBurst (cWB), which are both described in Abadie et al.(2010a), searched for transients (bursts) without assuming a spe-cific waveform morphology. The Multi-Band Template Analysis(MBTA) (Marion 2004), searched for signals from coalesc-ing compact binaries. Triggers were ranked by their “detectionstatistic”, a figure of merit for each analysis, known asΩ, η, andρcombined, respectively. The statisticsη for cWB andρcombinedforMBTA are related to the amplitude SNR of the signal acrossall interferometers whileΩ is related to the Bayesian likelihoodof a GW signal being present. Triggers with a detection statis-tic above a nominal threshold, and occurring in times where allthree detectors were operating normally, were recorded in theGravitational-wave Candidate Event Database (GraCEDb).

The trigger generators also produced likelihood maps overthe sky (skymaps), indicating the location from which the signalwas most likely to have originated. A brief introduction to eachtrigger generator is presented in Sects. 4.1.1 – 4.1.3.

4.1.1. Coherent WaveBurst

Coherent WaveBurst has been used in previous searches for GWbursts, such as Abbott et al. (2009b) and Abadie et al. (2010a).

The algorithm performs a time-frequency analysis of data inthewavelet domain. It coherently combines data from all detectorsto reconstruct the two GW polarization waveformsh+(t) andh×(t) and the source coordinates on the sky. A statistic is con-structed from the coherent terms of the maximum likelihood ra-tio functional (Flanagan & Hughes 1998; Klimenko et al. 2005)for each possible sky location, and is used to rank each lo-cation in a grid that covers the sky (skymap). A detailed de-scription of the likelihood analysis, the sky localizationstatisticand the performance of the cWB algorithm is published else-where (Klimenko et al. 2011).

The search was run in two configurations which differ intheir assumptions about the GW signal. The “unconstrained”search places minimal assumptions on the GW waveform, whilethe “linear” search assumes the signal is dominated by a singleGW polarization state (Klimenko et al. 2011). While the uncon-strained search is more general, and is the configuration that wasused in previous burst analyses, the linear search has been shownto better estimate source positions for some classes of signals.For the online analysis, the two searches were run in parallel.

4.1.2. Omega Pipeline

In the Omega Pipeline search (Abadie et al. 2010a), triggersare first identified by performing a matched filter search witha bank of basis waveforms which are approximately (co)sine-Gaussians. The search assumes that a GW signal can be de-composed into a small number of these basis waveforms.Coincidence criteria are then applied, requiring a triggerwithconsistent frequency in another interferometer within a physi-cally consistent time window. A coherent Bayesian positionre-construction code (Searle et al. 2008, 2009) is then appliedtoremaining candidates. The code performs Bayesian marginaliza-tion over all parameters (time of arrival, amplitude and polariza-tion) other than direction. This results in a skymap providing theprobability that a signal arrived at any time, with any amplitudeand polarization, as a function of direction. Further marginaliza-tion is performed over this entire probability skymap to arrive ata single number, the estimated probability that a signal arrivedfrom any direction. TheΩ statistic is constructed from this num-ber and other trigger properties.

4.1.3. MBTA

The Multi-Band Template Analysis (MBTA) is a low-latencyimplementation of the matched filter search that is typically usedto search for compact binary inspirals (Marion 2004; Buskulic2010). In contrast to burst searches which do not assume anyparticular waveform morphology, MBTA specifically targetsthewaveforms expected from NS-NS, NS-BH and BH-BH inspi-rals. In this way it provides complementary coverage to the burstsearches described above.

The search uses templates computed from a second orderpost-Newtonian approximation for the phase evolution of thesignal, with component masses in the range 1–34M⊙ and a totalmass of< 35M⊙. However, triggers generated from templateswith both component masses larger than the plausible limit of theNS mass—conservatively taken to be 3.5 M⊙ for this check—were not considered for EM follow-up, since the optical emis-sion is thought to be associated with the merger of two neutronstars or with the disruption of a neutron star by a stellar-massblack hole.

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 8

Triggers from each interferometer are clustered and used tosearch for coincidence among the individual detectors. To gen-erate a candidate event for follow-up, triggers with consistentphysical parameters must be present in all three LIGO/Virgo in-terferometers. For each triple coincident trigger, the skylocationwas estimated using the time delay between detector sites andthe amplitude of the signal measured in each detector (Fairhurst2009). Before the observing period, a set of simulated gravita-tional wave signals was used to measure the distribution of er-rors in recovering the time delays and signal amplitudes. Thesky localization algorithm then uses these distributions to assignprobabilities to each pixel on the sky.

4.2. Estimating False Alarm Rates

The primary quantity used to decide whether an event should beconsidered a candidate for follow-ups was its FAR, the averagerate at which noise fluctuations create events with an equal orgreater value of the detection statistic. For the winter run, a FARof less than 1 event per day of livetime was required to send animaging request to the ground-based telescopes, with a higherthreshold for Swift. For the autumn run, the FAR threshold was0.25 events per day of livetime for most telescopes, with stricterrequirements for sending triggers to Palomar Transient Factoryand Swift. Livetime is here defined as time all three interferom-eters were simultaneously collecting usable science data.

As in previous all-sky burst searches, e.g. Abbott et al.(2009b) and Abadie et al. (2010a), the FAR for the two burstpipelines was evaluated using the time-shift method. In thismethod, artificial time shifts, between one second and a few hun-dred seconds, are applied to the strain series of one or more in-terferometers, and the shifted data streams are analyzed with theregular coherent search algorithm. The shifted data provide anestimate of the background noise trigger rate without any truecoincident gravitational wave signals. During the online anal-ysis, at least 100 time shifts were continuously evaluated withlatencies between 10 minutes and several hours. The FAR ofeach event candidate was evaluated with the most recent avail-able time shifts.

The MBTA pipeline evaluated the FAR analytically based onsingle interferometer trigger rates, rather than using time shifts.This is computationally simpler than the burst method. It isvalidsince MBTA is a coincident rather than a coherent analysis, andallows the FAR to be evaluated with data from the minutes im-mediately preceding the trigger time (Marion 2004).

4.3. Online Data Quality

A number of common occurrences may make a stretch of inter-ferometer data unsuitable for sensitive GW searches. Examplesinclude times of large seismic disturbance, non-standard inter-ferometer configurations, and temporary saturations of variousphotodiodes in the interferometer sensing and control system.To mark such times, monitor programs analyze auxiliary datatoproduce lists of abnormal time segments with low latency. Whena trigger was identified, it was automatically checked againstthese lists; triggers which occurred in stretches of unacceptabledata were automatically rejected.

4.4. Manual Event Validation

In addition to automated checks on data quality, significanttrig-gers were manually vetted. Trigger alerts were broadcast tocol-

laboration members via e-mail, text message, a website, andinthe interferometer control rooms as audio alarms. For each alert,a low-latency pipeline expert conferred with personnel at each ofthe three observatory sites to validate the event. Pipelineexpertsand scientists monitoring data on-site provided 24/7 coverage in8 hour shifts. Assigned personnel confirmed the automated dataquality results, checked plots for obvious abnormalities,and ver-ified that there were no known disturbances at any of the threeobservatory sites.

The intention of manual event validation was to veto spuri-ous events caused by known non-GW mechanisms that have notbeen caught by low-latency data quality cuts, not to remove ev-ery non-GW trigger. In fact, at current sensitivities, mostor allof the triggers are unlikely to represent true astrophysical events.The trade-off for this additional check on the quality of the trig-gers was added latency (usually 10 to 20 minutes) between trig-ger identification and reporting to the EM observatories. Itispossible that as the search matures in the Advanced LIGO/Virgoera the validation process can be fully automated.

5. Choosing Fields to Observe

The uncertainty associated with GW position estimates, ex-pected to be several tens of square degrees (Fairhurst 2009), islarge compared to the FOV of most astronomical instruments.Moreover, the likely sky regions calculated from interferometerdata may be irregularly shaped, or even contain several disjointregions. It is impractical to image these entire regions given alimited amount of observing time for a given instrument. Thereis thus a need to carefully prioritize fields, or tiles, of an instru-ment to optimize the likelihood of imaging the true gravitationalwave source.

The LUMIN software package was created to gather GWtriggers from the three trigger generators, and use the skymapsand locations of known galaxies to select fields for each opti-cal or radio instrument to observe. In addition, LUMIN includestools that were used to facilitate trigger validation (Sect. 4.4) andcommunication with robotic telescopes. Fields for observationwith the Swift XRT and UVOT were selected with slightly dif-ferent criteria by a separate software package, the Gravitationalto Electro-Magnetic Processor (GEM). During the testing pro-cess, GEM also applied the tiling criteria for optical telescopesto simulated skymaps, and so provided an important consistencycheck between LUMIN and GEM.

5.1. Galaxy Catalog

The Gravitational Wave Galaxy Catalog (GWGC) (White et al.2011) was created to help this and future searches quickly iden-tify nearby galaxies.

The catalog contains up-to-date information compiled fromthe literature on sky position, distance, blue magnitude, ma-jor and minor diameters, position angle and galaxy type for53,225 galaxies ranging out to 100 Mpc, as well as 150 MilkyWay globular clusters. White et al. (2011) compared the catalogwith an expected blue light distribution derived from SDSS dataand concluded that the GWGC is nearly complete out to∼40Mpc. The catalog improves on the issue of multiple entries forthe same galaxy suffered by previous catalogs by creating theGWGC from a subset of 4 large catalogs, each of which listsa unique Principal Galaxy Catalogue (PGC) number for everygalaxy (Paturel et al. 1989). The catalogs used were: an updatedversion of the Tully Nearby Galaxies Catalog (Tully 1987), the

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 9

Catalog of Neighboring Galaxies (Karachentsev et al. 2004), theV8k catalog (Tully et al. 2009), and HyperLEDA (Paturel et al.2003). Also included is a list of 150 known Milky Way globularclusters (Harris 1996). These are all available freely online, but alocal, homogeneous list is essential for rapid follow-up purposes.

5.2. Weighting and Tiling Algorithm

To make use of the galaxy catalog, and choose tiles for each GWtrigger, similar algorithms have been implemented in the GEMand LUMIN software packages.

The position information from the trigger generators (seeSect. 4.1) is encoded in skymaps that assign a likelihood to each0.4 × 0.4 pixel in a grid covering the sky. In practice, onlythe 1000 most likely pixels are retained, limiting the sky area toroughly 160 square degrees. The search volume is further lim-ited by keeping only objects in the catalog with an estimateddis-tance of less than 50 Mpc, as the current sensitivity of the GWdetectors makes it unlikely that binaries containing a neutron starwould be detectable beyond this distance. Approximately 8%ofthe pixels in an average skymap contain a local galaxy or globu-lar cluster listed in the GWGC catalog.

For burst triggers, the tiling algorithms treat the luminosityof each galaxy or globular cluster as a prior for its likelihoodto host a GW emitting event. The blue light luminosity is usedas a proxy for star formation, indicating the presence of mas-sive stars that may be GW burst progenitors themselves and mayevolve into compact binaries that eventually merge. In addition,weak sources of GWs are assumed to be more numerous thanstrong sources, so that a closer galaxy should contain morede-tectable sources than a more distant galaxy of the same mass(Nuttall & Sutton 2010). This leads to assigning the followinglikelihood to each pixel:

P ∝∑

i

MiLDi

(1)

whereL is the likelihood based only on the GW data, andMandD are the blue light luminosity (a rough proxy for mass) anddistance of the associated galaxy or globular cluster. The sum isover all the objects associated with a particular pixel (which willbe 0 or 1 galaxy for the majority of pixels). Extended nearbysources which have a major axis larger than the pixel size havetheir mass divided evenly over each pixel falling within theel-lipse of the disk defined by their major and minor axes. Oncethis calculation is performed for each pixel, the entire skymap isrenormalized to a total likelihood equal to unity.

Unlike the burst algorithms, MBTA assumes the GW sourceis a merging binary, and estimates some of the source’s physicalparameters for each trigger. This allows the galaxy catalogto beapplied in a slightly different way. Each interferometer measuresa quantity known aseffective distance

Deff = D

F2+

(

1+ cos2 ι2

)2

+ F2× cos2 ι

−1/2

, (2)

whereD is the actual distance to the source,ι is the inclinationangle between the direction to the observer and the angular mo-mentum vector of the binary, andF+ andF× are the antenna re-sponse functions of the particular interferometer. The importantfeature of the effective distance is that it is always greater than orequal to the true distance to the source. For each MBTA trigger

the galaxy catalog is then only considered out to the smallest ef-fective distance measured for that trigger, with a maximum pos-sible effective distance of 50 Mpc. After the catalog is downse-lected in this way, each pixel is weighted by the fraction of thecatalog’s total mass contained in that pixel, i.e.

P =∑

i

Mfraci L, (3)

with the sum over all galaxies associated with the pixel, and∑

k Mfrack = 1 for a sum over the downselected catalog.

These procedures require a pixel’s coordinates to be consis-tent with a known galaxy’s location to be targeted by telescopes.However, in the case that the skymap does not intersect withany galaxies in the catalog, the likelihood from the GW skymapalone is used as each pixel’s likelihood (P = L). In practice, thisis a very rare occurrence and only happens in the case of a verywell-localized skymap.

The actual pointing coordinates requested for each telescopeare selected to maximize the total containedP summed over pix-els within the FOV. If multiple pointings are allowed with thesame instrument, additional tiles with the next highest rankingare then selected. The tile selection process is illustrated in Fig.3.

5.3. Galaxy Targeting for Small-Field Instruments

The logic used for selecting pointings for the Swift satellite wassimilar to that of ground-based telescopes, except that, becausethe narrower Swift FOV required greater precision, care wastaken to ensure the target galaxies were within the selectedfield.The coordinates supplied to Swift for follow-up were those ofthe matched galaxy itself in cases where there was only a singlegalaxy in a pixel, but the center of the 0.4×0.4 pixel in caseswhere the central coordinates of an extended source were outsidethe pixel or there were multiple galaxies in the pixel. Sincefewerfollow-ups were allowed using Swift than with other scopes,aminimum requirement was placed on the statisticP containedwithin the pixels selected for X-ray observation.

Zadko and Liverpool Telescope also have relatively narrowfields. For these telescopes, no attempt was made to capture mul-tiple galaxies in a single field. Instead, the weighting scheme inEqn. 1 was applied to each galaxy rather than each pixel, and thecenter coordinates of the top ranked galaxies were passed totheobservatories.

6. Performance Study

6.1. Simulated Waveform Injections

An ensemble of simulated GW signals was generated to mea-sure the effectiveness of the reconstruction and follow-up pro-cedures. For the Omega and cWB burst pipelines, these “soft-ware injections” were a mix of ad hoc sine-Gaussian, Gaussian,and white noise burst waveforms similar in type and distri-bution to those used in previous LIGO/Virgo all-sky analyses(Abbott et al. 2009b; Abadie et al. 2010a). While these wave-forms are not based on specific astrophysical models, they doa good job of characterizing detector response for signals inspecific frequency ranges (sine-Gaussians) and broadband sig-nals (white-noise bursts). For MBTA (see Sect. 4.1.3), injectionswere drawn from NS-NS and NS-BH inspiral waveforms witha range of parameters. To emulate a realistic spatial distribu-tion, each injection was calculated with a source distance and

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 10

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Fig. 3. The weighting and tiling process for a simulated signal reconstructed by cWB. The skymap is shown in the left panel withthe highest likelihood regions in red, and lower ranked pixels in blue, along with galaxy locations marked as black circles. The rightpanel shows the location and approximate size of the three chosen QUEST tiles, along with the locations of pixels that areretainedafter weighting by the galaxy catalog. The injection location is caught by the southernmost tile, and is marked with an asterisk.

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Fig. 4. Plots of typical uncertainty region sizes for the Omega (top) and unconstrained cWB (bottom) pipelines, as a function ofGW strain amplitude at Earth, for various waveform types. The “searched area” is the area of the skymap with a likelihood valuegreater than the likelihood value at the true source location before the galaxy catalog is used to further limit the search region. Thesolid line with symbols represents the median (50%) performance, while the upper and lower dashed lines show the 75% and 25%quartile values. Near the detection threshold (hrss ∼ 10−21 Hz−1/2) , uncertainty regions are typically between 10 and 100 squaredegrees. The Omega pipeline performs poorly on white noise bursts but exceptionally well on sine-Gaussians because it is designedto identify signals that are well-localized in frequency space.

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 11

direction inside a randomly selected galaxy from the GWGCand the simulated GW amplitudes were weighted to be inverselyproportional to distance. Only galaxies within 50 Mpc were in-cluded in the simulation, with weighting factors applied sothatthe probability of originating from each galaxy was proportionalto its blue light luminosity. The simulation set and the analysisused the same catalog, so the results presented in Figures 6 –8make the assumption that the blue light luminosity distributionof galaxies in the GWGC is a good tracer of GW sources in thelocal universe. Signals were superimposed on real LIGO-Virgogravitational wave data taken between August and December2009.

While performance studies in this paper were done usingsoftware injections, a relatively small number of tests in whicha signal was physically put into the interferometer via actuators(“hardware injections”) were also performed, providing anaddi-tional cross-check.

6.2. Testing Results

Because the skymap likelihood regions are often irregularlyshaped, the size of the uncertainty region is characterizedby the“searched area”, defined as the angular area of the skymap withlikelihood greater than the likelihood at the true source location.The median searched area as a function of signal strength is plot-ted for both cWB and Omega Pipeline in Fig. 4. Here, gravita-tional wave amplitudes are expressed in terms of their root-sum-squared amplitude:

hrss≡

√

∫

(

|h+ (t) |2 + |h× (t) |2)

dt (4)

whereh+ (t) andh× (t) are the plus- and cross-polarization strainfunctions of the wave. Sinceh is a dimensionless quantity,hrssis given in units of Hz−1/2. For this data, signals near the detec-tion threshold would havehrss ∼ 10−21 Hz−1/2, roughly corre-sponding to the cWB statisticη ∼ 5 (Abadie et al. 2010a). Thesesignals were typically localized with median search areas of sev-eral tens of square degrees. The coherent position reconstructionalgorithms are “tuned” to localize these near-threshold signalsas accurately as possible; as a result, some of the plots reveala degradation in algorithm performance for very loud signals.Median searched area is shown for MBTA in Fig. 5, as a func-tion of the combined SNR of the signal:

ρcombined≡

√

ρ2H1 + ρ

2L1 + ρ

2V1, (5)

whereρ2H1, ρ2

L1, andρ2V1 are the single detector SNRs seen in the

Hanford, Livingston and Virgo instruments, respectively.The simulated GW signals described above were also used

to test the tiling software in order to determine the successratefor imaging the correct sky location with realistic detector noise,reconstructed skymap shapes, and telescope FOVs. The LUMINsoftware package was used to determine pointings for ground-based telescopes and GEM was used for Swift.

Some of the results of this simulation can be seen in Fig.6. The results are plotted as a function of the ranking statisticused by each pipeline. On the y-axis, the “Fraction of triggersimaged” represents the fraction of triggers with the given detec-tion statistic that have the correct image location included withinthe selected tiles. Given a GW trigger, the success rate plottedin Fig. 6 estimates the odds of choosing the right sky position.In this figure, note that the “whole skymap” is limited to 160

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Fig. 5. Plots of uncertainty region sizes for the MBTA pipelineas a function of combined SNR (ρcombined). The solid line withsymbols represents the median (50%) performance, while theupper and lower dashed lines show the 75% and 25% quartilevalues. The expected detection threshold is aroundρcombined ∼

12.

square degrees, and so does not always include the true sourcelocation. The thresholds for initiating follow-ups variedwith thecondition of the interferometers, but was typically around3.0for Ω, η = 3.5 for cWB, andρcombined = 10 for MBTA. Thecomplex behavior of the Omega efficiency curve is related to theuse of a hybrid detection statistic which utilizes different meth-ods depending on SNR range. Clearly, events with SNR near thethreshold for triggering follow-ups, the most likely scenario forthe first detections, are the most difficult to localize.

Example efficiency curves for the burst simulation are shownin Fig. 7. The efficiency for each marker on the plot is calculatedas the fraction of signals for which the injected location wassuccessfully imaged, for anhrss range centered on the marker.Specifically, we require that:

1. The trigger’s ranking statistic is higher than the threshold,which is chosen to enforce a FAR of about 1 GW trigger perday of livetime.

2. The true source location is included in one of the chosentiles. Five tiles are allowed for Swift, three tiles for theQUEST camera, and one tile for all other telescopes.

Note that efficiencies in this figure do not reach unity even forloud events primarily due to the difficulty of correctly localizingGWs in some regions of the sky where the antenna response ofone or more interferometers is poor.

The efficiencies produced with these criteria are upper lim-its on what would be detected in a real search. They assume thatthe EM transient is very bright, and will always be detected if thecorrect sky location is imaged. The quoted efficiencies do not ac-count for the fact that some chosen tiles will not be observeddueto restrictions from weather, instrument availability, proximity tothe Sun or Moon, or the application of a manual veto. The ex-act behavior of the efficiency curves will vary depending on themorphologies of the simulation waveforms selected. Finally, thechosen GW trigger FAR of one event per day presumes the falsealarm fraction from the EM transient classification pipeline willbe low enough to make a coincidence significant.

Nevertheless, these curves provide a measure of the potentialfor joint EM/GW searches. If the number of incidental EM tran-

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 12

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Fig. 6. Success rates for the tile selection process based on unconstrained cWB (left), Omega (right), and MBTA (bottom) skymaps.An injection recovered with the detection statistic shown on the horizontal axis is considered a success if the correct source locationis included in one of the chosen tiles. Typical thresholds for follow-up areΩ=3.0,η=3.5, andρcombined=10. Each tile is 1.85×1.85,the FOV of both the ROTSE and TAROT telescopes. Statistical uncertainties are small with respect to the markers.

sients in the observed fields can be understood and controlled,then the addition of EM data can effectively increase the searchsensitivity to very weak GW signals. For occasional strong GWsignals, the plots suggest that only a few pointings of a telescopeare enough to image the true location with better than 50% effi-ciency.

6.3. Calibration Uncertainty

Uncertainty in the calibration of GW detectors may impact theability to correctly choose the right fields to observe with EMinstruments. To estimate the potential detriment to pointing, wegenerated a second set of simulated burst signals, with eachsig-nal including some level of miscalibration corresponding to re-alistic calibration errors. Before being added to detectornoise,each astrophysical signal was scaled in amplitude by a factor be-tween 0.85 and 1.15, and shifted in time by between−150 and150 µs. The exact amplitude and time “jitter” were randomlyselected from flat distributions for each signal entering each de-tector. The bounds of the distribution of values for the timing andamplitude jitter were chosen to match preliminary estimates forthe LIGO and Virgo calibration error budgets around 150 Hz forthe 2009–2010 run. Well above this frequency, the actual timing

errors are likely less than this model; the simulation is conserva-tive in this sense.

Some of the results of this simulation, with the cWB algo-rithm, may be seen in Fig. 8. The success rate is shown for theentire pipeline, assuming one pointing of a 1.85 × 1.85 FOV,three pointings of the QUEST FOV, and five pointings of a SwiftFOV. The curves are shown both with and without the effectsof calibration uncertainty. For the low SNR signals that arethemost likely for first detections,η . 10, the efficiency is withina few percent with and without the calibration uncertainty.Thisis expected, since at low signal to noise ratio, timing uncertaintyfrom detector noise is larger than timing uncertainty due tocali-bration (Fairhurst 2009). However, for louder signals, theabilityto correctly choose the right sky location is seen to be modestlyimpacted by the accuracy of the calibration.

7. Summary

Mergers of compact binary systems containing neutron stars, aswell as some other energetic astrophysical events, are expectedto emit observable transients in both the gravitational wave andelectromagnetic channels. Observing populations of jointsignalswould likely reveal many details of the GW sources, and couldeven constrain cosmological models.

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 13

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Fig. 7. Fractional success as a function of strain at Earth for combinations of the Omega and cWB burst pipelines. Success ratesassume 5 pointings for each event for Swift (left) and 3 for QUEST (right). Fractional success is end-to-end from triggering bypipeline to successful pointing, with a threshold for follow-up approximating a FAR of one per day. The “Logical Or” curve countsa success if either linear cWB or Omega correctly localized the event, effectively doubling the allowed number of tiles. Some curvesshow degraded performance for very loud signals because thealgorithms are tuned to optimize performance for weaker events closeto the detection threshold. Statistical uncertainties aresmall with respect to the markers.

η0 5 10 15 20 25 30 35 40

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Fig. 8. Fractional success rate for simulated gravitational wave signals with (dashed) and without (solid) including calibrationuncertainties. QUEST results assume 3 pointings, Swift results assume 5 pointings and TAROT/ROTSE results assume 1 pointing.

During 2009 and 2010, the LIGO and Virgo collaborationspartnered with a large, heterogeneous group of EM observato-ries to jointly seek transient signals. X-ray, optical, andradioobservatories collected follow-up observations to GW triggersthat were delivered with∼30 minutes of latency. Analysis of themulti-instrument data set is currently in progress, and theresultsof the search for jointly observed transients will be published ata later date.

A Monte Carlo study of the GW data analysis algorithmsused in the low latency pipeline demonstrated the ability oftheLIGO/Virgo network to localize transient GW events on the sky.Localization ability depends strongly on the SNR of the GWsignal; lower SNR signals are more difficult to localize, but arealso the more likely scenario for the first detections. Signals withSNR near the detection threshold were localized with mediansky areas between 10 and 100 square degrees. After limitingthe search to known galaxies and Milky Way globular clusterswithin the detection range of the GW observatories, the simu-lation shows that the correct location of signals detected near

threshold can be imaged with 30-50% success with three fieldsof size 1.85 × 1.85, for instance. Moreover, the ability to im-age the source position is seen to be only marginally impactedby realistic levels of calibration uncertainty.

This search establishes a baseline for low-latency analysiswith the next-generation GW detectors Advanced LIGO andAdvanced Virgo. Installation of these second-generation detec-tors is already in progress, with observations expected to beginaround 2015. Developing a low-latency response to GW triggersrepresents the first steps toward solving the many logistical andtechnical challenges that must be overcome to collect prompt,multiwavelength, EM observations of GW source progenitors.The integration of GW and EM observatories is likely to con-tinue to develop over the next few years as the scientific com-munity prepares to utilize the many opportunities promisedbythe impending global network of advanced GW detectors.

Acknowledgements. The authors gratefully acknowledge the support of theUnited States National Science Foundation for the construction and op-eration of the LIGO Laboratory, the Science and Technology FacilitiesCouncil of the United Kingdom, the Max-Planck-Society, andthe State of

LSC+Virgo+others: First prompt search for EM counterparts to GW transients 14

Niedersachsen/Germany for support of the construction and operation of theGEO600 detector, and the Italian Istituto Nazionale di Fisica Nucleare andthe French Centre National de la Recherche Scientifique for the construc-tion and operation of the Virgo detector. The authors also gratefully acknowl-edge the support of gravitational wave research by these agencies and by theAustralian Research Council, the International Science Linkages program of theCommonwealth of Australia, the Council of Scientific and Industrial Research ofIndia, the Istituto Nazionale di Fisica Nucleare of Italy, the Spanish Ministeriode Educacion y Ciencia, the Conselleria d’Economia Hisenda i Innovacio ofthe Govern de les Illes Balears, the Foundation for Fundamental Research onMatter supported by the Netherlands Organisation for Scientific Research, thePolish Ministry of Science and Higher Education, the FOCUS Programmeof Foundation for Polish Science, the Royal Society, the Scottish FundingCouncil, the Scottish Universities Physics Alliance, The National Aeronauticsand Space Administration, the Carnegie Trust, the Leverhulme Trust, the Davidand Lucile Packard Foundation, the Research Corporation, and the Alfred P.Sloan Foundation. The authors acknowledge support for TAROT from theFrench Ministere des Affaires Etrangeres and Ministere de l’EnseignementSuperieur et de la Recherche. The work with Swift was partially supportedthrough a NASA grant/cooperative agreement number NNX09AL61G to theMassachusetts Institute of Technology. The contribution from the “Pi of the Sky”group was financed by the Polish Ministry of Science in 2008-2011 as a researchproject. This document has been assigned LIGO Laboratory document numberLIGO-P1000061-v19.

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LSC+Virgo+others: First prompt search for EM counterparts to GW transients 15

1 LIGO - California Institute of Technology, Pasadena, CA 91125,USA

2 California State University Fullerton, Fullerton CA 92831USA3 SUPA, University of Glasgow, Glasgow, G12 8QQ, UK4 Laboratoire d’Annecy-le-Vieux de Physique des Particules(LAPP),

Universite de Savoie, CNRS/IN2P3, F-74941 Annecy-Le-Vieux,France

5 INFN, Sezione di Napolia; Universita di Napoli ’Federico II’b

Complesso Universitario di Monte S.Angelo, I-80126 Napoli;Universita di Salerno, Fisciano, I-84084 Salernoc, Italy

6 LIGO - Livingston Observatory, Livingston, LA 70754, USA7 Albert-Einstein-Institut, Max-Planck-Institut fur

Gravitationsphysik, D-30167 Hannover, Germany8 Leibniz Universitat Hannover, D-30167 Hannover, Germany9 University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA

10 Stanford University, Stanford, CA 94305, USA11 University of Florida, Gainesville, FL 32611, USA12 Louisiana State University, Baton Rouge, LA 70803, USA13 University of Birmingham, Birmingham, B15 2TT, UK14 INFN, Sezione di Romaa; Universita ’La Sapienza’b, I-00185 Roma,

Italy15 LIGO - Hanford Observatory, Richland, WA 99352, USA16 Albert-Einstein-Institut, Max-Planck-Institut fur

Gravitationsphysik, D-14476 Golm, Germany17 Montana State University, Bozeman, MT 59717, USA18 European Gravitational Observatory (EGO), I-56021 Cascina (PI),

Italy19 Syracuse University, Syracuse, NY 13244, USA20 University of Western Australia, Crawley, WA 6009, Australia21 LIGO - Massachusetts Institute of Technology, Cambridge, MA

02139, USA22 Laboratoire AstroParticule et Cosmologie (APC) Universite Paris

Diderot, CNRS: IN2P3, CEA: DSM/IRFU, Observatoire de Paris,10 rue A.Domon et L.Duquet, 75013 Paris - France

23 Columbia University, New York, NY 10027, USA24 INFN, Sezione di Pisaa; Universita di Pisab; I-56127 Pisa;

Universita di Siena, I-53100 Sienac, Italy25 Nikhef, Science Park, Amsterdam, the Netherlandsa; VU University

Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, theNetherlandsb

26 The University of Texas at Brownsville and Texas SouthmostCollege, Brownsville, TX 78520, USA

27 San Jose State University, San Jose, CA 95192, USA28 Moscow State University, Moscow, 119992, Russia29 LAL, Universite Paris-Sud, IN2P3/CNRS, F-91898 Orsaya; ESPCI,

CNRS, F-75005 Parisb, France30 NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA31 The Pennsylvania State University, University Park, PA 16802, USA32 Universite Nice-Sophia-Antipolis, CNRS, Observatoire de la Cote

d’Azur, F-06304 Nicea; Institut de Physique de Rennes, CNRS,Universite de Rennes 1, 35042 Rennesb, France

33 Laboratoire des Materiaux Avances (LMA), IN2P3/CNRS, F-69622Villeurbanne, Lyon, France

34 Washington State University, Pullman, WA 99164, USA35 INFN, Sezione di Perugiaa; Universita di Perugiab, I-06123

Perugia,Italy36 INFN, Sezione di Firenze, I-50019 Sesto Fiorentinoa; Universita

degli Studi di Urbino ’Carlo Bo’, I-61029 Urbinob, Italy37 University of Oregon, Eugene, OR 97403, USA38 Laboratoire Kastler Brossel, ENS, CNRS, UPMC, UniversitePierre

et Marie Curie, 4 Place Jussieu, F-75005 Paris, France39 Rutherford Appleton Laboratory, HSIC, Chilton, Didcot, Oxon

OX11 0QX, UK40 IM-PAN 00-956 Warsawa; Astronomical Observatory Warsaw

University 00-478 Warsawb; CAMK-PAN 00-716 Warsawc;

Białystok University 15-424 Białystokd; IPJ 05-400 Swierk-Otwocke; Institute of Astronomy 65-265 Zielona Goraf , Poland

41 University of Maryland, College Park, MD 20742 USA42 University of Massachusetts - Amherst, Amherst, MA 01003, USA43 The University of Mississippi, University, MS 38677, USA44 Canadian Institute for Theoretical Astrophysics, University of

Toronto, Toronto, Ontario, M5S 3H8, Canada45 Tsinghua University, Beijing 100084 China46 University of Michigan, Ann Arbor, MI 48109, USA47 Charles Sturt University, Wagga Wagga, NSW 2678, Australia48 Caltech-CaRT, Pasadena, CA 91125, USA49 INFN, Sezione di Genova; I-16146 Genova, Italy50 Pusan National University, Busan 609-735, Korea51 Carleton College, Northfield, MN 55057, USA52 Australian National University, Canberra, ACT 0200, Australia53 The University of Melbourne, Parkville, VIC 3010, Australia54 Cardiff University, Cardiff, CF24 3AA, UK55 INFN, Sezione di Roma Tor Vergataa; Universita di Roma Tor

Vergata, I-00133 Romab; Universita dell’Aquila, I-67100 L’Aquilac,Italy

56 University of Salerno, I-84084 Fisciano (Salerno), Italy and INFN(Sezione di Napoli), Italy

57 The University of Sheffield, Sheffield S10 2TN, UK58 RMKI, H-1121 Budapest, Konkoly Thege Miklos ut 29-33,

Hungary59 INFN, Gruppo Collegato di Trentoa and Universita di Trentob, I-

38050 Povo, Trento, Italy; INFN, Sezione di Padovac and Universitadi Padovad, I-35131 Padova, Italy

60 Inter-University Centre for Astronomy and Astrophysics, Pune -411007, India

61 University of Minnesota, Minneapolis, MN 55455, USA62 California Institute of Technology, Pasadena, CA 91125, USA63 Northwestern University, Evanston, IL 60208, USA64 The University of Texas at Austin, Austin, TX 78712, USA65 Eotvos Lorand University, Budapest, 1117 Hungary66 University of Adelaide, Adelaide, SA 5005, Australia67 University of Szeged, 6720 Szeged, Dom ter 9, Hungary68 Embry-Riddle Aeronautical University, Prescott, AZ 86301USA69 National Institute for Mathematical Sciences, Daejeon 305-390,

Korea70 Perimeter Institute for Theoretical Physics, Ontario, Canada, N2L

2Y571 National Astronomical Observatory of Japan, Tokyo 181-8588,

Japan72 Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain73 Korea Institute of Science and Technology Information, Daejeon

305-806, Korea74 University of Southampton, Southampton, SO17 1BJ, UK75 Institute of Applied Physics, Nizhny Novgorod, 603950, Russia76 Lund Observatory, Box 43, SE-221 00, Lund, Sweden77 Hanyang University, Seoul 133-791, Korea78 Seoul National University, Seoul 151-742, Korea79 University of Strathclyde, Glasgow, G1 1XQ, UK80 Southern University and A&M College, Baton Rouge, LA 7081381 University of Rochester, Rochester, NY 14627, USA82 Rochester Institute of Technology, Rochester, NY 14623, USA83 Hobart and William Smith Colleges, Geneva, NY 14456, USA84 University of Sannio at Benevento, I-82100 Benevento, Italy and

INFN (Sezione di Napoli), Italy85 Louisiana Tech University, Ruston, LA 71272, USA86 McNeese State University, Lake Charles, LA 70609 USA87 University of Washington, Seattle, WA, 98195-4290, USA88 Andrews University, Berrien Springs, MI 49104 USA89 Trinity University, San Antonio, TX 78212, USA90 Southeastern Louisiana University, Hammond, LA 70402, USA91 “Pi of the Sky” and the Andrzej Soltan Institute for Nuclear Studies,

Hoza 69, 00-681 Warsaw, Poland92 Research School of Astronomy & Astrophysics, Mount Stromlo

Observatory, Cotter Road, Weston Creek, ACT 2611, Australia93 Observatoire de Haute Provence, CNRS, FR 04870 St-Michel

l’Observatoire, France94 Centre d’Etude Spatial des Rayonnements (CESR), 9 Avenue du

colonel Roche, 31028 TOULOUSE Cedex 4, France95 NASA Einstein Fellow96 Jodrell Bank Center for Astrophysics, University of Manchester,

Manchester M13 9PL, UK97 Liverpool John Moores University, Liverpool L3 2AJ, UK98 Astronomical Institute “Anton Pannekoek”, University of

Amsterdam, 1090 GE Amsterdam, The Netherlands