Astronomy
&
Astrophysics
A&A 539, A124 (2012)
DOI: 10.1051/0004-6361/201118219
c ESO 2012
Implementation and testing of the first prompt search
for gravitational wave transients with electromagnetic counterparts
The LIGO Scientific Collaboration and Virgo Collaboration: J. Abadie1 , B. P. Abbott1 , R. Abbott1 , T. D. Abbott2 , M. Abernathy3 , T. Accadia4 , F. Acernese5,7 , C. Adams8 , R. Adhikari1 ,
C. Affeldt9,10 , M. Agathos11 , P. Ajith1 , B. Allen9,13,10 , G. S. Allen14 , E. Amador Ceron13 , D. Amariutei15 , R. S. Amin16 , S. B. Anderson1 , W. G. Anderson13 , K. Arai1 , M. A. Arain15 ,
M. C. Araya1 , S. M. Aston17 , P. Astone18 , D. Atkinson20 , P. Aufmuth10,9 , C. Aulbert9,10 , B. E. Aylott17 , S. Babak21 , P. Baker22 , G. Ballardin23 , S. Ballmer24 , D. Barker20 , F. Barone5,7 ,
B. Barr3 , P. Barriga25 , L. Barsotti26 , M. Barsuglia27 , M. A. Barton20 , I. Bartos28 , R. Bassiri3 , M. Bastarrika3 , A. Basti29,30 , J. Batch20 , J. Bauchrowitz9,10 , Th. S. Bauer11 , M. Bebronne4 ,
B. Behnke21 , M.G. Beker11 , A. S. Bell3 , A. Belletoile4 , I. Belopolski28 , M. Benacquista32 , J. M. Berliner20 , A. Bertolini9,10 , J. Betzwieser1 , N. Beveridge3 , P. T. Beyersdorf33 ,
I. A. Bilenko34 , G. Billingsley1 , J. Birch8 , R. Biswas32 , M. Bitossi29 , M. A. Bizouard35 , E. Black1 , J. K. Blackburn1 , L. Blackburn37 , D. Blair25 , B. Bland20 , M. Blom11 , O. Bock9,10 ,
T. P. Bodiya26 , C. Bogan9,10 , R. Bondarescu38 , F. Bondu40 , L. Bonelli29,30 , R. Bonnand41 , R. Bork1 , M. Born9,10 , V. Boschi29 , S. Bose42 , L. Bosi43 , B. Bouhou27 , S. Braccini29 ,
C. Bradaschia29 , P. R. Brady13 , V. B. Braginsky34 , M. Branchesi45,46 , J. E. Brau47 , J. Breyer9,10 , T. Briant48 , D. O. Bridges8 , A. Brillet39 , M. Brinkmann9,10 , V. Brisson35 , M. Britzger9,10 ,
A. F. Brooks1 , D. A. Brown24 , A. Brummit49 , T. Bulik51,52 , H. J. Bulten11,12 , A. Buonanno56 , J. Burguet–Castell57 , O. Burmeister9,10 , D. Buskulic4 , C. Buy27 , R. L. Byer14 , L. Cadonati58 ,
G. Cagnoli45 , E. Calloni5,6 , J. B. Camp37 , P. Campsie3 , J. Cannizzo37 , K. Cannon59 , B. Canuel23 , J. Cao60 , C. D. Capano24 , F. Carbognani23 , S. Caride61 , S. Caudill16 , M. Cavaglià62 ,
F. Cavalier35 , R. Cavalieri23 , G. Cella29 , C. Cepeda1 , E. Cesarini46 , O. Chaibi39 , T. Chalermsongsak1 , E. Chalkley17 , P. Charlton63 , E. Chassande-Mottin27 , S. Chelkowski17 , Y. Chen64 ,
A. Chincarini65 , A. Chiummo23 , H. S. Cho66 , N. Christensen67 , S. S. Y. Chua68 , C. T. Y. Chung69 , S. Chung25 , G. Ciani15 , F. Clara20 , D. E. Clark14 , J. Clark70 , J. H. Clayton13 , F. Cleva39 ,
E. Coccia71,72 , P.-F. Cohadon48 , C. N. Colacino29,30 , J. Colas23 , A. Colla18,19 , M. Colombini19 , A. Conte18,19 , R. Conte74 , D. Cook20 , T. R. Corbitt26 , M. Cordier33 , N. Cornish22 ,
A. Corsi1 , C. A. Costa16 , M. Coughlin67 , J.-P. Coulon39 , P. Couvares24 , D. M. Coward25 , D. C. Coyne1 , J. D. E. Creighton13 , T. D. Creighton32 , A. M. Cruise17 , A. Cumming3 ,
L. Cunningham3 , E. Cuoco23 , R. M. Cutler17 , K. Dahl9,10 , S. L. Danilishin34 , R. Dannenberg1 , S. D’Antonio71 , K. Danzmann9,10 , V. Dattilo23 , B. Daudert1 , H. Daveloza32 , M. Davier35 ,
G. Davies70 , E. J. Daw75 , R. Day23 , T. Dayanga42 , R. De Rosa5,6 , D. DeBra14 , G. Debreczeni76 , J. Degallaix9,10 , W. Del Pozzo11 , M. del Prete78 , T. Dent70 , V. Dergachev1 , R. DeRosa16 ,
R. DeSalvo1 , V. Dhillon75 , S. Dhurandhar80 , L. Di Fiore5 , A. Di Lieto29,30 , I. Di Palma9,10 , M. Di Paolo Emilio71,73 , A. Di Virgilio29 , M. Díaz32 , A. Dietz4 , J. DiGuglielmo9,10 ,
F. Donovan26 , K. L. Dooley15 , S. Dorsher81 , M. Drago77,78 , R. W. P. Drever82 , J. C. Driggers1 , Z. Du60 , J.-C. Dumas25 , S. Dwyer26 , T. Eberle9,10 , M. Edgar3 , M. Edwards70 , A. Effler16 ,
P. Ehrens1 , G. Endrőczi76 , R. Engel1 , T. Etzel1 , K. Evans3 , M. Evans26 , T. Evans8 , M. Factourovich28 , V. Fafone71,72 , S. Fairhurst70 , Y. Fan25 , B. F. Farr83 , W. Farr83 , D. Fazi83 ,
H. Fehrmann9,10 , D. Feldbaum15 , I. Ferrante29,30 , F. Fidecaro29,30 , L. S. Finn38 , I. Fiori23 , R. P. Fisher38 , R. Flaminio41 , M. Flanigan20 , S. Foley26 , E. Forsi8 , L. A. Forte5 , N. Fotopoulos1 ,
J.-D. Fournier39 , J. Franc41 , S. Frasca18,19 , F. Frasconi29 , M. Frede9,10 , M. Frei84,85 , Z. Frei86 , A. Freise17 , R. Frey47 , T. T. Fricke16 , J. K. Fridriksson26 , D. Friedrich9,10 , P. Fritschel26 ,
V. V. Frolov8 , P. J. Fulda17 , M. Fyffe8 , M. Galimberti41 , L. Gammaitoni43,44 , M. R. Ganija87 , J. Garcia20 , J. A. Garofoli24 , F. Garufi5,6 , M. E. Gáspár76 , G. Gemme65 , R. Geng60 ,
E. Genin23 , A. Gennai29 , L. Á. Gergely88 , S. Ghosh42 , J. A. Giaime16,6 , S. Giampanis13 , K. D. Giardina8 , A. Giazotto29 , C. Gill3 , E. Goetz9,10 , L. M. Goggin13 , G. González16 ,
M. L. Gorodetsky34 , S. Goßler9,10 , R. Gouaty4 , C. Graef9,10 , M. Granata27 , A. Grant3 , S. Gras25 , C. Gray20 , N. Gray3 , R. J. S. Greenhalgh49 , A. M. Gretarsson89 , C. Greverie39 ,
R. Grosso32 , H. Grote9,10 , S. Grunewald21 , G. M. Guidi45,46 , C. Guido8 , R. Gupta80 , E. K. Gustafson1 , R. Gustafson61 , T. Ha90 , B. Hage10,9 , J. M. Hallam17 , D. Hammer13 , G. Hammond3 ,
J. Hanks20 , C. Hanna1,91 , J. Hanson8 , J. Harms82 , G. M. Harry26 , I. W. Harry70 , E. D. Harstad47 , M. T. Hartman15 , K. Haughian3 , K. Hayama92 , J.-F. Hayau40 , J. Heefner1 ,
A. Heidmann48 , M. C. Heintze15 , H. Heitmann32 , P. Hello35 , M. A. Hendry3 , I. S. Heng3 , A. W. Heptonstall1 , V. Herrera14 , M. Hewitson9,10 , S. Hild3 , D. Hoak58 , K. A. Hodge1 , K. Holt8 ,
J. Homan26 , T. Hong64 , S. Hooper25 , D. J. Hosken87 , J. Hough3 , E. J. Howell25 , B. Hughey13 , S. Husa57 , S. H. Huttner3 , T. Huynh-Dinh8 , D. R. Ingram20 , R. Inta68 , T. Isogai67 ,
A. Ivanov1 , K. Izumi92 , M. Jacobson1 , H. Jang93 , P. Jaranowski53 , W. W. Johnson16 , D. I. Jones94 , G. Jones70 , R. Jones3 , L. Ju25 , P. Kalmus1 , V. Kalogera83 , I. Kamaretsos70 ,
S. Kandhasamy81 , G. Kang93 , J. B. Kanner37,56,⋆ , E. Katsavounidis26 , W. Katzman8 , H. Kaufer9,10 , K. Kawabe20 , S. Kawamura92 , F. Kawazoe9,10 , W. Kells1 , D. G. Keppel1 ,
Z. Keresztes88 , A. Khalaidovski9,10 , F. Y. Khalili34 , E. A. Khazanov95 , B. K. Kim93 , C. Kim96 , D. Kim25 , H. Kim9,10 , K. Kim97 , N. Kim14 , Y. M. Kim66 , P. J. King1 , M. Kinsey38 ,
D. L. Kinzel8 , J. S. Kissel26 , S. Klimenko15 , K. Kokeyama17 , V. Kondrashov1 , R. Kopparapu38 , S. Koranda13 , W. Z. Korth1 , I. Kowalska51 , D. Kozak1 , V. Kringel9,10 , S. Krishnamurthy83 ,
B. Krishnan21 , A. Królak50,54 , G. Kuehn9,10 , R. Kumar3 , P. Kwee10,9 , M. Laas-Bourez25 , P. K. Lam68 , M. Landry20 , M. Lang38 , B. Lantz14 , N. Lastzka9,10 , C. Lawrie3 , A. Lazzarini1 ,
P. Leaci21 , C. H. Lee66 , H. M. Lee98 , N. Leindecker14 , J. R. Leong9,10 , I. Leonor47 , N. Leroy35 , N. Letendre4 , J. Li60 , T. G. F. Li11 , N. Liguori77,78 , P. E. Lindquist1 , N. A. Lockerbie99 ,
D. Lodhia17 , M. Lorenzini45 , V. Loriette36 , M. Lormand8 , G. Losurdo45 , J. Luan64 , M. Lubinski20 , H. Lück9,10 , A. P. Lundgren38 , E. Macdonald3 , B. Machenschalk9,10 , M. MacInnis26 ,
D. M. Macleod70 , M. Mageswaran1 , K. Mailand1 , E. Majorana18 , I. Maksimovic36 , N. Man39 , I. Mandel26 , V. Mandic81 , M. Mantovani29,31 , A. Marandi14 , F. Marchesoni43 , F. Marion4 ,
S. Márka28 , Z. Márka28 , A. Markosyan14 , E. Maros1 , J. Marque23 , F. Martelli45,46 , I. W. Martin3 , R. M. Martin15 , J. N. Marx1 , K. Mason26 , A. Masserot4 , F. Matichard26 , L. Matone28 ,
R. A. Matzner84 , N. Mavalvala26 , G. Mazzolo9,10 , R. McCarthy20 , D. E. McClelland68 , P. McDaniel26 , S. C. McGuire100 , G. McIntyre1 , J. McIver58 , D. J. A. McKechan70 ,
G. D. Meadors61 , M. Mehmet9,10 , T. Meier8,9 , A. Melatos69 , A. C. Melissinos101 , G. Mendell20 , D. Menendez38 , R. A. Mercer13 , S. Meshkov1 , C. Messenger70 , M. S. Meyer8 , H. Miao25 ,
C. Michel41 , L. Milano5,6 , J. Miller68 , Y. Minenkov71 , V. P. Mitrofanov34 , G. Mitselmakher15 , R. Mittleman26 , O. Miyakawa92 , B. Moe13 , P. Moesta21 , M. Mohan23 , S. D. Mohanty32 ,
S. R. P. Mohapatra58 , D. Moraru20 , G. Moreno20 , N. Morgado41 , A. Morgia71,72 , T. Mori92 , S. Mosca5,6 , K. Mossavi9,10 , B. Mours4 , C. M. Mow–Lowry68 , C. L. Mueller15 , G. Mueller15 ,
S. Mukherjee32 , A. Mullavey68 , H. Müller-Ebhardt9,10 , J. Munch87 , D. Murphy28 , P. G. Murray3 , A. Mytidis15 , T. Nash1 , L. Naticchioni18,19 , R. Nawrodt3 , V. Necula15 , J. Nelson3 ,
I. Neri43,44 , G. Newton3 , A. Nishizawa92 , F. Nocera23 , D. Nolting8 , L. Nuttall70 , E. Ochsner13 , J. O’Dell49 , E. Oelker26 , G. H. Ogin1 , J. J. Oh90 , S. H. Oh90 , R. G. Oldenburg13 ,
B. O’Reilly8 , R. O’Shaughnessy13 , C. Osthelder1 , C. D. Ott64 , D. J. Ottaway87 , R. S. Ottens15 , H. Overmier8 , B. J. Owen38 , A. Page17 , G. Pagliaroli71,73 , L. Palladino71,73 , C. Palomba18 ,
Y. Pan56 , C. Pankow15 , F. Paoletti29,23 , M. A. Papa21,13 , M. Parisi5,6 , A. Pasqualetti23 , R. Passaquieti29,30 , D. Passuello29 , P. Patel1 , M. Pedraza1 , P. Peiris85 , L. Pekowsky24 , S. Penn102 ,
C. Peralta21 , A. Perreca24 , G. Persichetti5,6 , M. Phelps1 , M. Pichot39 , M. Pickenpack9,10 , F. Piergiovanni45,46 , M. Pietka53 , L. Pinard41 , I. M. Pinto103 , M. Pitkin3 , H. J. Pletsch9,10 ,
M. V. Plissi3 , R. Poggiani29,30 , J. Pöld9,10 , F. Postiglione74 , M. Prato65 , V. Predoi70 , L. R. Price1 , M. Prijatelj9,10 , M. Principe103 , S. Privitera1 , R. Prix9,10 , G. A. Prodi77,78 ,
L. G. Prokhorov34 , O. Puncken9,10 , M. Punturo43 , P. Puppo18 , V. Quetschke32 , F. J. Raab20 , D. S. Rabeling11,12 , I. Rácz76 , H. Radkins20 , P. Raffai86 , M. Rakhmanov32 , C. R. Ramet8 ,
B. Rankins62 , P. Rapagnani18,19 , S. Rapoport68 , V. Raymond83 , V. Re71,72 , K. Redwine28 , C. M. Reed20 , T. Reed104 , T. Regimbau39 , S. Reid3 , D. H. Reitze15 , F. Ricci18,19 , R. Riesen8 ,
K. Riles61 , N. A. Robertson1,3 , F. Robinet35 , C. Robinson70 , E. L. Robinson21 , A. Rocchi71 , S. Roddy8 , C. Rodriguez83 , M. Rodruck20 , L. Rolland4 , J. Rollins28 , J. D. Romano32 ,
R. Romano5,7 , J. H. Romie8 , D. Rosińska52,54 , C. Röver9,10 , S. Rowan3 , A. Rüdiger9,10 , P. Ruggi23 , K. Ryan20 , H. Ryll9,10 , P. Sainathan15 , M. Sakosky20 , F. Salemi9,10 , A. Samblowski9,10 ,
L. Sammut69 , L. Sancho de la Jordana57 , V. Sandberg20 , S. Sankar26 , V. Sannibale1 , L. Santamaría1 , I. Santiago-Prieto3 , G. Santostasi105 , B. Sassolas41 , B. S. Sathyaprakash70 , S. Sato92 ,
P. R. Saulson24 , R. L. Savage20 , R. Schilling9,10 , S. Schlamminger106 , R. Schnabel9,10 , R. M. S. Schofield47 , B. Schulz9,10 , B. F. Schutz21,70 , P. Schwinberg20 , J. Scott3 , S. M. Scott68 ,
A. C. Searle1 , F. Seifert1 , D. Sellers8 , A. S. Sengupta1 , D. Sentenac23 , A. Sergeev95 , D. A. Shaddock68 , M. Shaltev9,10 , B. Shapiro26 , P. Shawhan56 , D. H. Shoemaker26 , A. Sibley8 ,
X. Siemens13 , D. Sigg20 , A. Singer1 , L. Singer1 , A. M. Sintes57 , G. R. Skelton13 , B. J. J. Slagmolen68 , J. Slutsky16 , J. R. Smith2 , M. R. Smith1 , N. D. Smith26 , R. J. E. Smith17 ,
K. Somiya64 , B. Sorazu3 , J. Soto26 , F. C. Speirits3 , L. Sperandio71,72 , M. Stefszky68 , A. J. Stein26 , E. Steinert20 , J. Steinlechner9,10 , S. Steinlechner9,10 , S. Steplewski42 , A. Stochino1 ,
R. Stone32 , K. A. Strain3 , S. E. Strigin34 , A. S. Stroeer32 , R. Sturani45,46 , A. L. Stuver8 , T. Z. Summerscales107 , M. Sung16 , S. Susmithan25 , P. J. Sutton70 , B. Swinkels23 , M. Tacca23 ,
L. Taffarello79 , D. Talukder42 , D. B. Tanner15 , S. P. Tarabrin9,10 , J. R. Taylor9,10 , R. Taylor1 , P. Thomas20 , K. A. Thorne8 , K. S. Thorne64 , E. Thrane81 , A. Thüring10,9 , C. Titsler38 ,
K. V. Tokmakov99 , A. Toncelli29,30 , M. Tonelli29,30 , O. Torre29,31 , C. Torres8 , C. I. Torrie1,3 , E. Tournefier4 , F. Travasso43,44 , G. Traylor8 , M. Trias57 , K. Tseng14 , D. Ugolini108 ,
K. Urbanek14 , H. Vahlbruch10,9 , G. Vajente29,30 , M. Vallisneri64 , J. F. J. van den Brand11,12 , C. Van Den Broeck11 , S. van der Putten11 , A. A. van Veggel3 , S. Vass1 , M. Vasuth76 ,
R. Vaulin26 , M. Vavoulidis35 , A. Vecchio17 , G. Vedovato79 , J. Veitch70 , P. J. Veitch87 , C. Veltkamp9,10 , D. Verkindt4 , F. Vetrano45,46 , A. Viceré45,46 , A. E. Villar1 , J.-Y. Vinet39 , S. Vitale89 ,
S. Vitale11 , H. Vocca43 , C. Vorvick20 , S. P. Vyatchanin34 , A. Wade68 , S. J. Waldman26 , L. Wallace1 , Y. Wan60 , X. Wang60 , Z. Wang60 , A. Wanner9,10 , R. L. Ward27 , M. Was35 , P. Wei24 ,
M. Weinert9,10 , A. J. Weinstein1 , R. Weiss26 , L. Wen64,25 , S. Wen8 , P. Wessels9,10 , M. West24 , T. Westphal9,10 , K. Wette9,10 , J. T. Whelan85 , S. E. Whitcomb1,25 , D. J. White75 ,
B. F. Whiting15 , C. Wilkinson20 , P. A. Willems1 , H. R. Williams38 , L. Williams15 , B. Willke9,10 , L. Winkelmann9,10 , W. Winkler9,10 , C. C. Wipf26 , A. G. Wiseman13 , H. Wittel9,10 ,
G. Woan3 , R. Wooley8 , J. Worden20 , J. Yablon83 , I. Yakushin8 , H. Yamamoto1 , K. Yamamoto9,10 , H. Yang64 , D. Yeaton-Massey1 , S. Yoshida109 , P. Yu13 , M. Yvert4 , A. Zadrożny54 ,
M. Zanolin89 , J.-P. Zendri79 , F. Zhang60 , L. Zhang1 , W. Zhang60 , Z. Zhang25 , C. Zhao25 , N. Zotov104 , M. E. Zucker26 , J. Zweizig1 ,
C. Akerlof61 , M. Boer17 , R. Fender94 , N. Gehrels37 , A. Klotz110 , E. O. Ofek82,111 , M. Smith38 , M. Sokolowski112 , B. W. Stappers113 , I. Steele114 ,
J. Swinbank115 , R. A. M. J. Wijers115 , and W. Zheng61
(Affiliations can be found after the references in the electronic version)
Received 6 October 2011 / Accepted 22 December 2011
Article published by EDP Sciences
A124, page 1 of 15
A&A 539, A124 (2012)
ABSTRACT
Aims. A transient astrophysical event observed in both gravitational wave (GW) and electromagnetic (EM) channels would yield rich scientific
rewards. A first program initiating EM follow-ups to possible transient GW events has been developed and exercised by the LIGO and Virgo
community in association with several partners. In this paper, we describe and evaluate the methods used to promptly 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 Sep. 2 to Oct. 20, 2010), a low-latency analysis pipeline was used to
identify GW event candidates and to reconstruct maps of possible sky locations. A catalog of nearby galaxies and Milky Way globular clusters was
used to select the most promising sky positions to be imaged, and this directional information was delivered to EM observatories with time lags
of about thirty minutes. A Monte Carlo simulation has been used to evaluate the low-latency GW pipeline’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 tens of square degrees,
while neutron star/neutron star inspirals and neutron star/black hole inspirals were localized to a few hundred square degrees. Localization precision
improves for moderately stronger signals. The correct sky location of signals well above threshold and originating from nearby galaxies may be
observed with ∼50% or better probability with a few pointings of wide-field telescopes.
Key words. gravitational waves – methods: observational
1. Introduction
2. Motivation
The Laser Interferometer Gravitational-wave Observatory
(LIGO) (Abbott et al. 2009a) and Virgo (Accadia et al.
2011a) have taken significant steps toward gravitational wave
(GW) astronomy over the past decade. The LIGO Scientific
Collaboration operates two LIGO observatories in the US along
with the GEO 600 detector (Lück et al. 2010) in Germany.
Together with Virgo, located in Italy, they form a detector network capable of detecting GW signals arriving from all directions. Their most recent joint data taking run was between
July 2009 and October 2010. GEO 600 and Virgo are currently operating during summer 2011, while the LIGO interferometers have been decommissioned for the upgrade to the
next-generation Advanced LIGO detectors (Harry et al. 2010),
expected to be operational around 2015. Virgo will also be
upgraded to become Advanced Virgo (Acernese et al. 2008).
Additionally, the new LCGT detector (Kuroda & The LCGT
Collaboration 2010) is planned in Japan. These “advanced era”
detectors are expected to detect compact binary coalescences,
possibly at a rate of dozens per year, after reaching design sensitivity (Abadie et al. 2010c).
Detectable, transient GW signals in the LIGO/Virgo frequency band require bulk motion of mass on short time scales.
Emission in other channels is also possible in many such rapidly
changing massive systems. This leads to the prospect that some
transient GW sources may have corresponding electromagnetic
(EM) counterparts which could be discovered with a low latency
response 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 scientific
rewards (see Sect. 2), but is technically challenging due to imperfect localization of the gravitational wave signal and uncertainty regarding the relative timing of the GW and EM emissions. This paper details our recent effort to construct a prompt
search for joint GW/EM sources between the LIGO/Virgo detector network and partner EM observatories (see Sect. 3). The
search was demonstrated during two periods of live LIGO/Virgo
running: the “winter” observing period in December 2009 and
January 2010 and the “autumn” observing period in September
and October 2010. We focus here on the design and performance
of software developed for rapid EM follow-ups of GW candidate events, as well as the procedures used to identify significant
GW triggers and to communicate the most likely sky locations
to partner EM observatories. The analysis of the observational
data is in progress, and will be the subject of future publications.
2.1. Sources
⋆
Corresponding author: J. Kanner, jonah.kanner@ligo.org
A124, page 2 of 15
A variety of EM emission mechanisms, both observed and theoretical, may occur in association with observable GW sources.
Characteristics of a few scenarios helped inform the design and
execution 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 black
holes are thought to be the most common sources of GW emission detectable with ground-based interferometers. Radiation of
energy and angular momentum causes the orbit to decay (inspiral) until the objects merge (Cutler et al. 1993). For a system
consisting of two neutron stars (NS-NS) or a neutron star and
a stellar-mass black hole (NS-BH), the inspiral stage produces
the most readily detectable GW signal. The energy flux reaching Earth depends on the inclination angle of the binary orbit
relative to the line of sight. The initial LIGO-Virgo network is
sensitive to optimally oriented NS-NS mergers from as far away
as 30 Mpc, and mergers between a NS and a 10 M⊙ black hole
out to 70 Mpc (Abadie et al. 2010c). Models of the stellar compact object population in the local universe estimate the rate of
NS-NS mergers detectable with initial detectors to be between
2 × 10−4 and 0.2 per year. 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 counterpart is likely. The final plunge radiates of order 1053 erg of gravitational binding energy as gravitational waves. If even a small
fraction of this energy escapes as photons in the observing band,
the resulting counterpart could be observable to large distances.
The EM transients that are likely to follow a NS-BH or NS-NS
merger are described below.
Short-hard gamma-ray bursts (SGRBs), which typically have
durations of 2 s or less, may be powered by NS-NS or NS-BH
mergers (Nakar 2007; Mészáros 2006; Piran 2004). Afterglows
of SGRBs have been observed in wavelengths from radio to
X-ray, and out to Gpc distances (Nakar 2007; Gehrels et al.
2009). Optical afterglows have been observed from a few tens
of seconds to a few days after the GRB trigger (see, for example, Klotz et al. 2009a), and fade with a power law t−α , where
α is between 1 and 1.5. At 1 day after the trigger time, the
apparent optical magnitude would be between 12 and 20 for a
source at 50 Mpc (Kann et al. 2011), comparable to the distance
to which LIGO and Virgo could detect the merger.
LSC+Virgo+others: First prompt search for GW transients with EM counterparts
Even if a compact binary coalescence is not observable in
gamma-rays, there is reason to expect it will produce an observable optical counterpart. Li & Paczyński (1998) suggested that,
during a NS-NS or NS-BH merger, some of the neutron star’s
mass is tidally ejected. In their model, the ejected neutron-rich
matter produces heavy elements through r-process nucleosynthesis, which subsequently decay and heat the ejecta, powering
an optical afterglow known as a kilonova. The predicted optical
emission is roughly isotropic, and so is observable regardless of
the orientation of the original binary system. This emission is
expected to peak after about one day, around magnitude 18 for a
source at 50 Mpc (Metzger et al. 2010), and then fade over the
course of a few days following the merger.
2.1.2. Stellar core collapse
Beyond the compact object mergers described above, some other
astrophysical processes are plausible sources of observable GW
emission. GW transients with unknown waveforms may be discovered by searching the LIGO and Virgo data for short periods
of excess power (bursts). The EM counterparts to some likely
sources of GW burst signals are described below.
Core-collapse supernovae are likely to produce some amount
of gravitational radiation, though large uncertainties still exist
in the expected waveforms and energetics. Most models predict
GW spectra that would be observable by initial LIGO and Virgo
from distances within some fraction of the Milky Way, but not
from the Mpc distances needed to observe GWs from another
galaxy (Ott 2009). Neutrino detectors such as SuperKamiokande
and IceCube should also detect a large number of neutrinos from
a Galactic supernova (Beacom & Vogel 1999; Halzen & Raffelt
2009; Leonor et al. 2010). Galactic supernovae normally would
be very bright in the optical band, but could be less than obvious
if obscured by dust or behind the Galactic center. Optical emission would first appear hours after the GW and neutrino signal
and would peak days to weeks later, fading over the course of
weeks or months.
Long-soft gamma-ray bursts (LGRBs) are believed to be associated with the core collapse of massive stars (Woosley 1993;
MacFadyen & Woosley 1999; Piran 2004; Woosley & Bloom
2006; Metzger et al. 2011). A large variety of possible GW emitting mechanisms within these systems have been proposed, with
some models predicting GW spectra that would be observable
from distances of a few Mpc with initial LIGO and Virgo (Fryer
et al. 2002; Kobayashi & Mészáros 2003a; Corsi & Mészáros
2009; Piro & Pfahl 2007; Korobkin et al. 2011; Kiuchi et al.
2011). The afterglows of LGRBs, like the afterglows of SGRBs,
typically show power law fading with α = 1−1.5. However, the
peak isotropic equivalent luminosity of LGRB afterglows is typically a factor of 10 brighter than SGRB afterglows (Nakar 2007;
Kann et al. 2010).
An off-axis or sub-energetic LGRB may also be observed as
an orphan afterglow or dirty fireball (Granot et al. 2002; Rhoads
2003). These transients brighten over the course of several days
or even weeks, depending on the observing band and viewing angle. Identifying orphan afterglows in large area surveys, such as
Rykoff et al. (2005), has proven difficult, but a GW trigger may
help distinguish orphan afterglows from other EM variability.
2.1.3. Other possible sources
Cosmic string cusps are another possible joint source of GW
(Siemens et al. 2006; Damour & Vilenkin 2000) and EM
(Vachaspati 2008) radiation. If present, their distinct GW signature will distinguish them from other sources. On the other
hand, even unmodeled GW emissions can be detected using GW
burst search algorithms, and such events may in some cases produce EM radiation either through internal dynamics or through
interaction with the surrounding medium. Thus, our joint search
methods 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 extracted from a joint GW/EM signal. In understanding the progenitor physics, the EM and GW signals are essentially complementary. The GW time series directly traces the bulk motion
of mass in the source, whereas EM emissions arising from outflows or their interaction with the interstellar medium give only
indirect information requiring inference and modeling. On the
other hand, observing an EM counterpart to a GW signal reduces
the uncertainty in the source position from degrees to arcseconds. This precise directional information can lead to identification of a host galaxy, and a measurement of redshift. Some specific questions that may be addressed with a collection of joint
GW/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 estimation of
astrophysical source parameters. For example, when attempting
parameter estimation with a bank of templates and a single data
stream, the source’s distance, inclination angle, and angular position are largely degenerate. A precise source position from an
EM counterpart would help break this degeneracy (Dalal et al.
2006; Nissanke et al. 2010). High precision parameter 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 merger
events will give strong evidence as to which class of source, if
either, is the source of SGRBs (Bloom et al. 2009). In addition,
if some neutron star mergers are the sources of SGRBs, a collection of joint EM/GW observations would allow an estimate of
the SGRB jet opening angle by comparing the number of merger
events with and without observable prompt EM emission, and
some information would be obtainable even from a single loud
event (Kobayashi & Mészáros 2003b; Seto 2007).
An ensemble of these observations could provide a novel
measurement of cosmological parameters. Analysis of the wellmodeled GW signal will provide a measurement of the luminosity distance to the source, while the redshift distance is measurable from the EM data. Taken together, they provide a direct 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 the
correct theory of gravity on macroscopic scales. Joint EM/GW
observations can also be used to test certain predictions of general relativity, such as the propagation speed and polarizations of
GWs (Will 2005; Yunes et al. 2010; Kahya 2011).
In the case that the transient GW source is not a binary
merger event, the combination of GW and EM information will
again prove very valuable. In this scenario, the gravitational
waveform will not be known a priori. Any distance estimate
would be derived from the EM data, which would then set the
overall scale for the energy released as GWs.
As in the merger case, the linking of a GW signal with a
known EM phenomenon will provide insight into the underlying
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A&A 539, A124 (2012)
physical process. For example, the details of the central engine
that drives LGRBs are unknown. The GW signal could give crucial clues to the motion of matter in the source, and potentially
distinguish between competing models. A similar insight into
the source mechanism could be achieved for an observation of
GW emission associated with a supernova. Rapid identification
may also allow observation of a supernova in its earliest 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 trigger would increase confidence that a truly astrophysical event
had been observed in the GW data. Using EM transients to help
distinguish low amplitude GW signals from noise events allows
a lowering of the detection threshold, as was done in searches
such as Abbott et al. (2010a). Kochanek & Piran (1993) estimated that the detectable amplitude could be reduced by as much
as a factor of 1.5, increasing the effective detector horizon distance (the maximum distance at which an optimally oriented and
located system could be detected) by the same factor and thus
increasing the detection rate by a factor of 3. In practice, the
actual improvement in GW sensitivity achieved by pairing EM
and GW observations depends on many factors unique to each
search, including details of the source model and data set, and so
is difficult to predict in advance.
In the case of a coincidence between a GW signal and a discovered EM transient, the joint significance may be calculated
by assuming that the backgrounds of the EM and GW search are
independent. The False Alarm Rate (FAR) of a GW/EM coincidence is the FAR of the GW signal, as described in Sect. 4.2,
times α, the expected fraction of observations associated with a
false or unrelated EM transient. The false alarm fraction α may
be estimated using fields from surveys not associated with GW
triggers. The measured value of α will depend heavily on the
telescope being used, the cuts selected in image analysis, galactic latitude of the source and other factors. For example, surveys with the Palomar Transient Factory require a sophisticated
classification mechanism for rejecting contaminants. Each set
of image subtractions covering 100−200 square degrees yields
∼105 candidates. Of these, 30−150 sources are selected after
imposing cuts optimized for the detection of fast evolving transients (Bloom et al. 2011). Using classification software designed for PTF data (Oarical) (Bloom et al. 2011), the selected
sources undergo an automatic classification as type “transient”
or “variable star” based on time-domain and context properties.
Promising candidates are selected for additional, spectroscopic
observation. Of the sources that are classified as transients,
and then followed up spectroscopically, ∼82% are supernovae
(Bloom et al. 2011). To use EM transience to improve confidence in a GW signal, the time-domain sky in the wavelength of
interest must be well understood. Transients that are found in directional and time coincidence with GW triggers would increase
confidence in the GW signal only if the chance of a similar, incidental coincidence is understood to be low (Kulkarni & Kasliwal
2009).
in this paper emphasizes capturing images as soon as possible
after the GW trigger, along with follow-up images over subsequent nights. The rates of stellar core-collapse and compact object mergers within our own galaxy are much less than one per
year, and so field selection was strongly weighted towards regions containing nearby galaxies.
The observations and theoretical models of EM transients
discussed above provided guidance when choosing the observing cadence. GRB optical afterglows have been observed during
the prompt emission phase (Klotz et al. 2009b) and up to many
hours after the trigger. For this search, the first attempt to image
the source position was made as soon as possible after validating a GW trigger. In both the kilonova (Li & Paczyński 1998)
and supernova (Ott 2009) models, some time lag exists between
the release of GW and EM emission, primarily due to the time
it takes the outflowing material to become optically thin. This
time lag may be from several hours for a kilonova, up to days
for a core-collapse supernova. Furthermore, Coward et al. (2011)
show that for GRBs that are off-axis, the optical afterglow may
not be visible until days after the burst. For these reasons, repeated observations over several nights are desirable. Also, the
light curves obtained by observing the same fields over multiple
nights are critical clues for discovering and classifying transient
sources.
Knowing where to look for the counterpart to a GW trigger is challenging. Directional estimates of low signal to noise
ratio (SNR) binary inspiral sources with the 2009–10 GW detector network have uncertainties of several tens of square degrees
(Fairhurst 2009). This suggests using telescopes with a field of
view (FOV) of at least a few square degrees if possible. Even
with such a “wide field” instrument, there is a striking mismatch
between the large area needing to be searched, and the size of a
single FOV.
The problem may be partially mitigated by making use of
the known mass distribution in the nearby universe. A search for
GW counterparts can dramatically reduce the needed sky coverage by focusing observations on galaxies within the distance
limits of the GW detectors (Kanner et al. 2008; Nuttall & Sutton
2010). Limiting the search area to known galaxies may also
improve the feasibility of identifying the true counterpart from
among other objects with time-varying EM emissions (Kulkarni
& Kasliwal 2009). Even within the Milky Way, a search may
emphasize known targets by seeking counterparts within globular clusters, where binary systems of compact objects may form
efficiently (O’Leary et al. 2007).
An emphasis on extragalactic and globular-cluster sources
has the potential drawback that any counterparts in the plane of
the Milky Way may be missed. Also, neutron star mergers that
occur at large distances from their host galaxies may not be observed, though the population with large kicks should be small
(Berger 2010; Kelley et al. 2010).
Our selection of fields to observe was weighted towards areas containing known galaxies within 50 Mpc. The utilized catalog of nearby galaxies and globular clusters, and the process for
selecting fields to observe, is described in Sect. 5.
3. GW and EM instruments
3.1. Gravitational wave detector network
2.4. Implications for search design
Characteristics of the target sources helped determine when and
where to seek the EM counterparts to GW event candidates. For
reasons discussed in this section, the search strategy presented
A124, page 4 of 15
The LIGO and Virgo detectors are based on Michelson-type
interferometers, with Fabry-Perot cavities in each arm and a
power recycling mirror between the laser and beamsplitter to
dramatically increase the power in the arms relative to a simple
LSC+Virgo+others: First prompt search for GW transients with EM counterparts
Michelson design. The GEO 600 detector uses a folded interferometer without Fabry-Perot arm cavities but with an additional
recycling mirror at the output to resonantly enhance the GW signal. As a gravitational wave passes through each interferometer,
it induces a “strain” (a minuscule change in length on the order
of 1 part in 1021 or less) on each arm of the interferometer due
to the quadrupolar perturbation of the spacetime metric. The interferometers are designed to measure the differential strain on
the two arms through interference of the laser light when the two
beams are recombined at the beam splitter, with the relative optical phase modulated by the passing gravitational wave (Abbott
et al. 2009a).
In 2009–2010 there were two operating LIGO interferometers, each with 4-km arms: H1, located near Hanford,
Washington, and L1, located in Livingston Parish, Louisiana1 .
Virgo (V1) has arms of length 3 km and is located near Cascina,
Italy. GEO 600 data was not used in the online search described
in this paper, but was available for offline reanalysis of promising event candidates. The large physical separation between the
instruments means that the effects of local environmental background can be mitigated by requiring a coincident signal in multiple interferometers. Each interferometer is most sensitive to
GW signals traveling parallel or anti-parallel to zenith, but the
antenna pattern varies gradually over the sky, so that the detectors are essentially all-sky monitors.
The EM follow-up program described in this paper was exercised during the 2009–2010 science runs. While single-detector
triggers had been generated with low latency in earlier science
runs for diagnostic and prototyping purposes, 2009–2010 was
the first time that a systematic search for GW transients using
the 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 network
used in 2009–10 was intentionally heterogeneous. However,
most of instruments had large fields of view to accommodate
the imprecise GW position estimate. The approximate location
of each EM observatory is shown in Fig. 1, and Tables 1 and 2
show some of the properties of each observatory.
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 camera mounted
on the 1.2 m Oschin Telescope at Palomar Observatory. A typical 60 s exposure detects objects with a limiting magnitude
R = 20.5. For the autumn observing period, the PTF team devoted ten fields over several nights at a target rate of 1 trigger for
every three weeks.
Pi of the Sky (Malek et al. 2009) observed using a camera
with a 400 square degree FOV and exposures to limiting magnitude 11–12. It was located in Koczargi Stare, near Warsaw.
The camera was a prototype for a planned system that will simultaneously image two steradians of sky. The target rate was
approximately 1 per week in the autumn run, followed up with
hundreds of 10 s exposures over several nights.
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 a site in
Western Australia. The latter option would greatly improve the source
localization capabilities of the network (Fairhurst 2011; Schutz 2011).
Fig. 1. A map showing the approximate positions of telescopes that participated in the project. The Swift satellite observatory is noted at an
arbitrary location. The image is adapted from a blank world map placed
in the public domain by P. Dlouhý.
The QUEST camera (Baltay et al. 2007), currently mounted
on the 1 m ESO Schmidt Telescope at La Silla Observatory,
views 9.4 square degrees of sky in each exposure. The telescope
is capable of viewing to a limiting magnitude of R ∼ 20. The
QUEST team devoted twelve 60 s exposures over several nights
for each trigger in both the winter and autumn periods, with a
target rate of 1 trigger per week.
ROTSE III (Akerlof et al. 2003) is a collection of four robotic
telescopes spread around the world, each with a 0.45 m aperture
and 3.4 square degree FOV. No filters are used, so the spectral
response is that of the CCDs, spanning roughly 400 to 900 nm.
The equivalent R band limiting magnitude is about 17 in a 20 s
exposure. The ROTSE team arranged for a series of thirty images
for the first night, and several images on following nights, for
each autumn run trigger, with a target rate of 1 trigger per week.
SkyMapper (Keller et al. 2007) is a survey telescope located
at Siding Spring observatory in Australia. The mosaic camera
covers 5.7 square degrees of sky in each field, and is mounted
on a 1.35 m telescope with a collecting area equivalent to an
unobscured 1.01 m aperture. It is designed to reach a limiting
magnitude g ∼ 21 (>7 sigma) in a 110 s exposure. SkyMapper
accepted triggers in the autumn run with a target rate of 1 per
week, with several fields collected for each trigger.
TAROT (Klotz et al. 2009a) operates two robotic 25 cm telescopes, one at La Silla in Chile and one in Calern, France. Like
the ROTSE III system, each TAROT instrument has a 3.4 square
degree FOV. A 180 second image with TAROT in ideal conditions has a limiting R magnitude of 17.5. During the winter run,
TAROT observed a single field during one night for each trigger. In the autumn run, the field selected for each trigger was
observed over several nights. TAROT accepted triggers with a
target rate of 1 per week.
Zadko Telescope (Coward et al. 2010) is a 1 m telescope located in Western Australia. The current CCD imager observes
fields of 0.15 square degrees down to magnitude ∼20 in the
R band for a typical 180 s exposure. For each accepted trigger
in the autumn run, Zadko repeatedly observed the five galaxies
considered most likely to host the source over several nights. The
target trigger rate for Zadko was one trigger per week.
The Liverpool telescope (Steele et al. 2004) is a 2 m
robotic telescope situated at the Observatorio del Roque de
Los Muchachos on La Palma. For this project the RATCam instrument, with a 21 square arcminute FOV, was used. This instrumentation allows a five minute exposure to reach magnitude
r′ = 21. This project was awarded 8 h of target-of-opportunity
time, which was split into 8 observations of 1 h each, with a
target rate of 1 trigger per week.
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A&A 539, A124 (2012)
Table 1. Partner instrument characteristic properties.
Name
Palomar Transient Factory
Pi of the Sky
QUEST
ROTSE III
SkyMapper
TAROT
Zadko Telescope
Liverpool Telescope
LOFAR
Swift
Swift
Band
Optical
Optical
Optical
Optical
Optical
Optical
Optical
Optical
Radio
X-ray
UV, Optical
FOV (square degrees)
7.3
400
9.4
3.4
5.7
3.4
0.15
0.0058
∼25
0.15
0.078
Aperture (m)
1.2
0.072
1
0.45
1.35
0.25
1
2
N/A
N/A
0.3
Exposure time (s)
60
10
60
20
110
180
180
3600
14 400
200−5000
200−5000
Limiting magnitude
20.5
11.5
20
17.5
21
17.5
20
21
N/A
N/A
24
Notes. Some characteristics of instruments involved in the search. The shown limiting magnitudes are estimates, assuming favorable observing
conditions.
Table 2. Partner instrument follow-up information.
Name
Palomar Transient Factory
Pi of the Sky
QUEST
ROTSE III
SkyMapper
TAROT
Zadko Telescope
Liverpool Telescope
LOFAR
Swift
Run
Autumn
Autumn
Both
Autumn
Autumn
Both
Autumn
Autumn
Autumn
Both
Tiles per trigger
10
1
3
1
∼9
1
5
1
1
5
Target alerts per week
1/3
1
1
1
1
1
1
1
1
1/4
Triggers imaged
1
1
5
5
3
3
2
1
2
2
Notes. Follow-up information for instruments involved in the search. Each instrument participated in either the autumn run, or both the winter
and autumn runs. The column marked “Tiles per Trigger” shows how many different field locations the instrument attempted to observe for each
accepted GW event candidate. The “Target Alerts Per Week” column shows that alerts were sent to PTF and Swift at a lower rate than the other
observatories. The final column shows the number of GW event candidates for which each instrument collected data.
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 Netherlands
but with stations across Europe. The array is sensitive to frequencies in the range of 30 to 80 MHz and 110 to 240 MHz, and can
observe multiple simultaneous beams, each with a FWHM varying with frequency up to a maximum of around 23◦ . During the
autumn run, LOFAR accepted triggers at a target rate of 1 per
week and followed up each with a four-hour observation in its
higher frequency band, providing a ∼25 square degree FOV.
Although not used in the prompt search during the science
run, the Expanded Very Large Array (Perley et al. 2011) was
used to follow up a few triggers after the run with latencies of 3
and 5 weeks.
The Swift satellite (Gehrels et al. 2004) carries three instruments, each in different bands. Swift granted several target of opportunity observations with two of these, the X-ray
Telescope (XRT) and UV/Optical Telescope (UVOT), for the
winter and autumn observing periods. The XRT is an imaging
instrument with 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 for each trigger that Swift accepted.
4. Trigger selection
The online analysis process which produced GW candidate triggers to be sent to telescopes is outlined in Fig. 2. After data and
information on data quality were copied from the interferometer
A124, page 6 of 15
Fig. 2. A simplified flowchart of the online analysis with approximate
time requirements for each stage. Data and information on data quality
were generated at the Hanford, Livingston, and Virgo interferometers
(H1, L1, and V1) and copied to centralized computer centers. The online event trigger generators produced coincident triggers which were
written into the GraCEDb archive. The LUMIN and GEM algorithms
selected statistically significant triggers from the archive and chose
pointing locations. Significant triggers generated alerts, and were validated manually. If no obvious problem was found, the trigger’s estimated coordinates were sent to telescopes for potential follow-up.
sites to computing centers, three different data analysis algorithms identified triggers and determined probability skymaps.
The process of downselecting this large collection of triggers to
the few event candidates that received EM follow-up is described
in this section.
After event candidates were placed in a central archive, additional software used the locations of nearby galaxies and Milky
Way globular clusters to select likely source positions (Sect. 5).
LSC+Virgo+others: First prompt search for GW transients with EM counterparts
Triggers were manually vetted, then the selected targets were
passed to partner observatories which imaged the sky in an attempt to find an associated EM transient. Studies demonstrating
the performance of this pipeline on simulated GWs are presented
in Sect. 7.
4.1. Trigger generation
Sending GW triggers to observatories with less than an hour latency represents a major shift from past LIGO/Virgo data analyses, which were reported outside the collaboration at soonest
several months after the data collection. Reconstructing source
positions requires combining the data streams from the LIGOVirgo network using either fully coherent analysis or a coincidence analysis of single-detector trigger times. A key step in latency reduction was the rapid data replication process, in which
data from all three GW observatory sites were copied to several
computing centers within a minute of collection.
For the EM follow-up program, three independent GW detection algorithms (trigger generators), ran promptly as data
became available, generating candidate triggers with latencies
between five and eight minutes. Omega Pipeline and coherent
WaveBurst (cWB), which are both described in Abadie et al.
(2010b), searched for transients (bursts) with only loose assumptions regarding waveform morphology. The Multi-Band
Template Analysis (MBTA) (Marion 2004), searched for signals from coalescing compact binaries. Triggers were ranked by
their “detection statistic”, a figure of merit for each analysis,
known as Ω, η, and ρcombined , respectively. The statistics η for
cWB and ρcombined for MBTA are related to the amplitude SNR
of the signal across all interferometers while Ω is related to the
Bayesian likelihood of a GW signal being present. Triggers with
a detection statistic above a nominal threshold, and occurring in
times where all three detectors were operating normally, were
recorded in the Gravitational-wave Candidate Event Database
(GraCEDb).
The trigger generators also produced likelihood maps over
the sky (skymaps), indicating the location from which the signal
was most likely to have originated. A brief introduction to each
trigger 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 GW
bursts, such as Abbott et al. (2009b) and Abadie et al. (2010b).
The algorithm performs a time-frequency analysis of data in
the wavelet domain. It coherently combines data from all detectors to reconstruct the two GW polarization waveforms h+ (t)
and h× (t) and the source coordinates on the sky. A statistic is
constructed from the coherent terms of the maximum likelihood ratio functional (Flanagan & Hughes 1998; Klimenko et al.
2005) for each possible sky location, and is used to rank each
location in a grid that covers the sky (skymap). A detailed description of the likelihood analysis, the sky localization statistic
and the performance of the cWB algorithm is published elsewhere (Klimenko et al. 2011).
The search was run in two configurations which differ in
their assumptions about the GW signal. The “unconstrained”
search places minimal assumptions on the GW waveform, while
the “linear” search assumes the signal is dominated by a single
GW polarization state (Klimenko et al. 2011). While the unconstrained search is more general, and is the configuration that was
used in previous burst analyses, the linear search has been shown
to 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. 2010b), triggers
are first identified by performing a matched filter search with
a bank of basis waveforms which are approximately (co)sineGaussians. The search assumes that a GW signal can be decomposed into a small number of these basis waveforms, and so is
most sensitive to signals with a small time-frequency volume.
Coincidence criteria are then applied, requiring a trigger with
consistent frequency in another interferometer within a physically consistent time window. A coherent Bayesian position reconstruction code (Searle et al. 2008, 2009) is then applied to
remaining candidates. The code performs Bayesian marginalization over all parameters (time of arrival, amplitude and polarization) other than direction. This results in a skymap providing the
probability that a signal arrived at any time, with any amplitude
and polarization, as a function of direction. Further marginalization is performed over this entire probability skymap to arrive at
a single number, the estimated probability that a signal arrived
from any direction. The Ω statistic is constructed from this number and other trigger properties.
4.1.3. MBTA
The Multi-Band Template Analysis (MBTA) is a low-latency
implementation of the matched filter search that is typically used
to search for compact binary inspirals (Marion 2004; Buskulic
2010). In contrast to burst searches which do not assume any
particular waveform morphology, MBTA specifically targets the
waveforms expected from NS-NS, NS-BH and BH-BH inspirals. In this way it provides complementary coverage to the burst
searches described above.
The search uses templates computed from a second order
post-Newtonian approximation for the phase evolution of the
signal, with component masses in the range 1−34 M⊙ and a total mass of <35 M⊙ . However, triggers generated from templates
with both component masses larger than the plausible limit of
the NS mass – conservatively taken to be 3.5 M⊙ for this check
– were not considered for EM follow-up, since the optical emission is thought to be associated with the merger of two neutron
stars or with the disruption of a neutron star by a stellar-mass
black hole.
Triggers from each interferometer are clustered and used to
search for coincidence among the individual detectors. To generate a candidate event for follow-up, triggers with consistent
physical parameters must be present in all three LIGO/Virgo interferometers. For each triple coincident trigger, the sky location
was estimated using the time delay between detector sites and
the amplitude of the signal measured in each detector (Fairhurst
2009). Before the observing period, a set of simulated gravitational wave signals was used to measure the distribution of errors in recovering the time delays and signal amplitudes. The
sky localization algorithm then uses these distributions to assign
probabilities to each pixel on the sky.
4.2. Estimating false alarm rates
The primary quantity used to decide whether an event should be
considered a candidate for follow-ups was its FAR, the average
rate at which noise fluctuations create events with an equal or
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greater value of the detection statistic. For the winter run, a FAR
of less than 1 event per day of livetime was required to send an
imaging request to the ground-based telescopes, with a higher
threshold for Swift. For the autumn run, the FAR threshold was
0.25 events per day of livetime for most telescopes, with stricter
requirements for sending triggers to Palomar Transient Factory
and Swift. Livetime is here defined as time all three interferometers were simultaneously collecting usable science data.
As in previous all-sky burst searches, e.g. Abbott et al.
(2009b) and Abadie et al. (2010b), the FAR for the two burst
pipelines was evaluated using the time-shift method. In this
method, artificial time shifts, between one second and a few hundred seconds, are applied to the strain series of one or more interferometers, and the shifted data streams are analyzed with the
regular coherent search algorithm. The shifted data provide an
estimate of the background noise trigger rate without any true
coincident gravitational wave signals. During the online analysis, at least 100 time shifts were continuously evaluated with
latencies between 10 min and several hours. The FAR of each
event candidate was evaluated with the most recent available
time shifts.
The MBTA pipeline evaluated the FAR analytically based on
single interferometer trigger rates, rather than using time shifts.
This is computationally simpler than the burst method. It is valid
since MBTA is a coincident rather than a coherent analysis, and
allows the FAR to be evaluated with data from the minutes immediately preceding the trigger time (Marion 2004).
trigger identification and reporting to the EM observatories. It is
possible that as the search matures in the Advanced LIGO/Virgo
era the validation process can be fully automated.
5. Choosing fields to observe
The uncertainty associated with GW position estimates, expected to be several tens of square degrees (Fairhurst 2009), is
large compared to the FOV of most astronomical instruments.
Moreover, the likely sky regions calculated from interferometer
data may be irregularly shaped, or even contain several disjoint
regions. It is impractical to image these entire regions given a
limited amount of observing time for a given instrument. There
is thus a need to carefully prioritize fields, or tiles, of an instrument to optimize the likelihood of imaging the true gravitational
wave source.
The LUMIN software package was created to gather GW
triggers from the three trigger generators, and use the skymaps
and locations of known galaxies to select fields for each optical or radio instrument to observe. In addition, LUMIN includes
tools that were used to facilitate trigger validation (Sect. 4.4) and
communication with robotic telescopes. Fields for observation
with the Swift XRT and UVOT were selected with slightly different criteria by a separate software package, the Gravitational
to Electro-Magnetic Processor (GEM). During the testing process, GEM also applied the tiling criteria for optical telescopes
to simulated skymaps, and so provided an important consistency
check between LUMIN and GEM.
4.3. Online data quality
A number of common occurrences may make a stretch of interferometer data unsuitable for sensitive GW searches. Examples
include times of large seismic disturbance, non-standard interferometer configurations, and temporary saturations of various
photodiodes in the interferometer sensing and control system.
To mark such times, monitor programs analyze auxiliary data to
produce lists of abnormal time segments with low latency. When
a trigger was identified, it was automatically checked against
these lists; triggers which occurred in stretches of unacceptable
data were automatically rejected. During this search, all three
GW detectors were simultaneously collecting science quality
data for roughly 45% of the time.
4.4. Manual event validation
In addition to automated checks on data quality, significant triggers were manually vetted. Trigger alerts were broadcast to collaboration members via e-mail, text message, a website, and in
the interferometer control rooms as audio alarms. For each alert,
a low-latency pipeline expert conferred with personnel at each of
the three observatory sites to validate the event. Pipeline experts
and scientists monitoring data on-site provided 24/7 coverage
in 8 h shifts. Assigned personnel confirmed the automated data
quality results, checked plots for obvious abnormalities, and verified that there were no known disturbances at any of the three
observatory sites.
The intention of manual event validation was to veto spurious events caused by known non-GW mechanisms that have
not been caught by low-latency data quality cuts, not to remove
every non-GW trigger. In fact, at current sensitivities, most or
all of the triggers are unlikely to represent true astrophysical
events. The trade-off for this additional check on the quality of
the triggers was added latency (usually 10 to 20 min) between
A124, page 8 of 15
5.1. Galaxy catalog
The Gravitational Wave Galaxy Catalog (GWGC) (White et al.
2011) was created to help this and future searches quickly identify nearby galaxies.
The catalog contains up-to-date information compiled from
the literature on sky position, distance, blue magnitude, major and minor diameters, position angle and galaxy type for
53 225 galaxies ranging out to 100 Mpc, as well as 150 Milky
Way globular clusters. White et al. (2011) compared the catalog with an expected blue light distribution derived from SDSS
data and concluded that the GWGC is nearly complete out to
∼40 Mpc. The catalog improves on the issue of multiple entries
for the same galaxy suffered by previous catalogs by creating
the GWGC from a subset of 4 large catalogs, each of which lists
a unique Principal Galaxy Catalogue (PGC) number for every
galaxy (Paturel et al. 1989). The catalogs used were: an updated
version of the Tully Nearby Galaxies Catalog (Tully 1987), the
Catalog of Neighboring Galaxies (Karachentsev et al. 2004), the
V8k catalog (Tully et al. 2009), and HyperLEDA (Paturel et al.
2003). Also included is a list of 150 known Milky Way globular
clusters (Harris 1996). These are all available freely online, but a
local, 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 GW
trigger, similar algorithms have been implemented in the GEM
and LUMIN software packages.
The position information from the trigger generators (see
Sect. 4.1) is encoded in skymaps that assign a likelihood to each
0.4◦ × 0.4◦ pixel in a grid covering the sky. In practice, only
the 1000 most likely pixels are retained, limiting the sky area to
roughly 160 square degrees. The search volume is further limited
LSC+Virgo+others: First prompt search for GW transients with EM counterparts
by keeping only objects in the catalog with an estimated distance
of less than 50 Mpc, as the current sensitivity of the GW detectors makes it unlikely that binaries containing a neutron star
would be detectable beyond this distance. Approximately 8% of
the pixels in an average skymap contain a local galaxy or globular cluster listed in the GWGC catalog.
For burst triggers, the tiling algorithms treat the luminosity
of each galaxy or globular cluster as a prior for its likelihood to
host a GW emitting event. The blue light luminosity is used as a
proxy for star formation, indicating the presence of massive stars
that may be GW burst progenitors themselves and may evolve
into compact binaries that eventually merge. In addition, weak
sources of GWs are assumed to be more numerous than strong
sources, so that a closer galaxy should contain more detectable
sources than a more distant galaxy of the same mass (Nuttall &
Sutton 2010). This leads to assigning the following likelihood to
each pixel:
P∝
Mi L
Di
i
(1)
where L is the likelihood based only on the GW data, and M
and D are the blue light luminosity (a rough proxy for mass) and
distance of the associated galaxy or globular cluster. The sum is
over all the objects associated with a particular pixel (which will
be 0 or 1 galaxy for the majority of pixels). Extended nearby
sources which have a major axis larger than the pixel size have
their mass divided evenly over each pixel falling within the ellipse of the disk defined by their major and minor axes. Once
this calculation is performed for each pixel, the entire skymap is
renormalized to a total likelihood equal to unity.
Unlike the burst algorithms, MBTA assumes the GW source
is a merging binary, and estimates some of the source’s physical
parameters for each trigger. This allows the galaxy catalog to be
applied in a slightly different way. Each interferometer measures
a quantity known as effective distance
The actual pointing coordinates requested for each telescope
are selected to maximize the total contained P summed over
pixels within the FOV. If multiple pointings are allowed with
the same instrument, additional tiles with the next highest ranking are 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 was
similar to that of ground-based telescopes, except that, because
the narrower Swift FOV required greater precision, care was
taken to ensure the target galaxies were within the selected field.
The coordinates supplied to Swift for follow-up were those of
the matched galaxy itself in cases where there was only a single galaxy in a pixel, but the center of the 0.4◦ × 0.4◦ pixel in
cases where the central coordinates of an extended source were
outside the pixel or there were multiple galaxies in the pixel.
Since fewer follow-ups were allowed using Swift than with other
scopes, a minimum requirement was placed on the statistic P
contained within the pixels selected for X-ray observation.
Zadko and Liverpool Telescope also have relatively narrow
fields. For these telescopes, no attempt was made to capture multiple galaxies in a single field. Instead, the weighting scheme in
Eq. (1) was applied to each galaxy rather than each pixel, and
the center coordinates of the top ranked galaxies were passed to
the observatories.
6. Observing strategy
6.1. Communication
where D is the actual distance to the source, ι is the inclination
angle between the direction to the observer and the angular momentum vector of the binary, and F+ and F× are the antenna response functions of the particular interferometer. The important
feature of the effective distance is that it is always greater than or
equal to the true distance to the source. For each MBTA trigger
the galaxy catalog is then only considered out to the smallest effective distance measured for that trigger, with a maximum possible effective distance of 50 Mpc. After the catalog is downselected in this way, each pixel is weighted by the fraction of the
catalog’s total mass contained in that pixel, i.e.
P=
(3)
Mifrac L,
After an event candidate passed manual inspection, a script was
launched to pass the GPS time and selected field center locations to the QUEST, ROTSE III, SkyMapper, TAROT, Zadko,
Liverpool Telescope, and LOFAR observatories. During the
autumn run, a total of five such alerts were sent. During the (earlier) winter run, 8 event candidates were passed to the TAROT
and QUEST observatories. The number of field locations passed
to each telescope for each GW event candidate are listed as the
“Tiles per Trigger” in Table 2. During the autumn run, in cases
where the fields selected for a particular instrument were unobservable due to daylight or latitude, no alert was sent to the
observatory. In most cases, alerts were sent via a direct socket
connection from a LIGO computing center at Caltech with IP
mask protection. Alerts to ROTSE III, SkyMapper, TAROT, and
Zadko used the format of GCN notices. Alerts to LOFAR and
the Liverpool Telescope used the VOEvent format (Williams &
Seaman 2006). For QUEST, the GPS time and field positions
were posted as ASCII tables to a password protected web site
which was regularly polled by the QUEST scheduler.
with the sum over all galaxies associated with the pixel, and
frac
= 1 for a sum over the downselected catalog.
k Mk
These procedures require a pixel’s coordinates to be consistent with a known galaxy’s location to be targeted by telescopes.
However, in the case that the skymap does not intersect with
any galaxies in the catalog, the likelihood from the GW skymap
alone is used as each pixel’s likelihood (P = L). In practice, this
is a very rare occurrence and only happens in the case of a very
well-localized skymap.
The Palomar Transient Factory received field locations and
GPS times using the VOEvent format via a socket connection,
but with a more restrictive FAR threshold than the other optical
telescopes, and so triggers were only sent to PTF if the on-call
team executed a separate script. Alerts to Swift also required extra action by the on-call team, who entered field coordinates in
an online form. The Pi of the Sky prototype telescope was engaged through automated e-mails and manual checks of a password protected web page.
Deff
⎡
⎤−1/2
⎢⎢⎢ 2 1 + cos2 ι 2
⎥
2
2 ⎥
⎢
= D ⎢⎣F+
+ F× cos ι⎥⎥⎥⎦
,
2
(2)
i
A124, page 9 of 15
30
30
20
20
10
10
0
Dec (Degrees)
Dec (Degrees)
A&A 539, A124 (2012)
−10
−20
−30
0
−10
−20
−30
−40
−40
−50
−50
−60
−60
−70
125
120
115
110
105
100
95
90
85
−70
125
120
115
110
105
100
95
90
85
RA (Degrees)
RA (Degrees)
Fig. 3. The weighting and tiling process for a simulated signal reconstructed by cWB. The skymap is shown in the left panel with the highest
likelihood regions in red, and lower ranked pixels in blue, along with galaxy locations marked as black circles. The right panel shows the location
and approximate size of the three chosen QUEST tiles, along with the locations of pixels that are retained after weighting by the galaxy catalog.
The injection location is caught by the southernmost tile, and is marked with an asterisk.
6.2. Telescope response
7. Performance study
The wide variety of telescopes involved in the search led to a
diversity of observing strategies, with each partnering group applying a different cadence. By design, most of the telescopes in
the network were robotic, and could respond to alerts without
human intervention. In a few cases this allowed response times
of less than a minute after an alert was sent, though response
times of a few hours were more typical due to wait time for targets to be overhead.
During the winter run, QUEST responded to three triggers,
making 2 exposures of each field on the night of the request.
TAROT responded to one winter run trigger, taking six images
on the night of the request. Swift also responded to one trigger in
the winter run, taking one exposure of each field following the
request, and then a second set of exposures on a later date to be
used as reference images.
For most observatories in the summer run, the observing plan
called for capturing a first image of the selected fields as rapidly
as possible, with follow-up observations every night or every
other night out to five days after the trigger time. For the optical
observatories, any night’s observation included 2 or more exposures for each field, to help eliminate asteroids, CCD artifacts,
and other contaminants from the data set. In addition, some fields
were imaged at later times, up to a month after the trigger time,
to provide reference images, or possibly to capture a light curve
with a late brightening time. TAROT, Zadko, PTF, QUEST, and
Pi of the Sky all followed this recipe. ROTSE executed a more
aggressive observing plan, collecting a set of 30 images in rapid
succession on the first night, and then sets of eight images on
each of 15 nights following the trigger with intervals of two days
on average. As in the winter run, Swift made one exposure of
each field following the trigger, and then collected a reference
image after a lag of several weeks. The Liverpool Telescope devoted roughly one hour of observation upon receiving a trigger,
and then collected reference images a few weeks after the trigger time. The LOFAR response was not automated. A telescope
operator made a single, four hour observation one to four days
after delivery of a trigger. SkyMapper also required manual intervention to respond to a trigger, and so responded on a best
effort basis.
7.1. Simulated Waveform Injections
A124, page 10 of 15
An ensemble of simulated GW signals was generated to measure the effectiveness of the reconstruction and follow-up procedures. For the Omega and cWB burst pipelines, these “software injections” were a mix of ad hoc sine-Gaussian, Gaussian,
and white noise burst waveforms similar in type and distribution
to those used in previous LIGO/Virgo all-sky analyses (Abbott
et al. 2009b; Abadie et al. 2010b). While these waveforms are
not based on specific astrophysical models, they do a good job
of characterizing detector response for signals in specific frequency ranges (sine-Gaussians) and broadband signals (whitenoise bursts). For MBTA (see Sect. 4.1.3), injections were drawn
from NS-NS and NS-BH inspiral waveforms with a range of parameters. To emulate a realistic spatial distribution, each injection was calculated with a source distance and direction inside
a randomly selected galaxy from the GWGC and the simulated
GW amplitudes were weighted to be inversely proportional to
distance. Only galaxies within 50 Mpc were included in the simulation, with weighting factors applied so that the probability of
originating from each galaxy was proportional to its blue light
luminosity. The simulation set and the analysis used the same
catalog, so the results presented in Figs. 6−8 make the assumption that the blue light luminosity distribution of galaxies in the
GWGC is a good tracer of GW sources in the local universe.
Signals were superimposed on real LIGO-Virgo gravitational
wave data taken between August and December 2009.
While performance studies in this paper were done using
software injections, a relatively small number of tests in which
a signal was physically put into the interferometer via actuators
(“hardware injections”) were also performed, providing an additional cross-check.
7.2. Testing results
Because the skymap likelihood regions are often irregularly
shaped, the size of the uncertainty region is characterized by the
“searched area”, defined as the angular area of the skymap with
likelihood greater than the likelihood at the true source location.
Median Searched Area (square degrees)
-18.5
3
10
102
10
1
10-1
-21.5
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-1/2
log10(hrss [Hz ])
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All Waveforms
3
10
White Noise Bursts
102
10
1
1053 Hz sine-Gaussian
102
10
1
10-2-22
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log10(hrss [Hz ])
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3
10
1053 Hz sine-Gaussian
102
10
1
10-1
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log10(hrss [Hz ])
-19
10-2-22
-21.5
-21 -20.5 -20 -19.5
-1/2
log10(hrss [Hz ])
The median searched area as a function of signal strength is plotted for both cWB and Omega Pipeline in Fig. 4. Here, gravitational wave amplitudes are expressed in terms of their root-sumsquared amplitude:
hrss ≡
|h+ (t) |2 + |h× (t) |2 dt
(4)
where h+ (t) and h× (t) are the plus- and cross-polarization strain
functions of the wave. Since h is a dimensionless quantity, hrss
is given in units of Hz−1/2 . For this data, signals near the detection threshold would have hrss ∼ 10−21 Hz−1/2 , roughly corresponding to the cWB statistic η ∼ 5 (Abadie et al. 2010b).
These signals were typically localized with median search areas
of several tens of square degrees. The coherent position reconstruction algorithms are “tuned” to localize these near-threshold
signals as accurately as possible; as a result, some of the plots
reveal a degradation in algorithm performance for very loud signals. Median searched area is shown for MBTA in Fig. 5, as a
function of the combined SNR of the signal:
ρcombined ≡
ρ2H1 + ρ2L1 + ρ2V1 ,
(5)
where ρ2H1 , ρ2L1 , and ρ2V1 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 success rate
for imaging the correct sky location with realistic detector noise,
reconstructed skymap shapes, and telescope FOVs. The LUMIN
software package was used to determine pointings for groundbased 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 statistic used
by each pipeline. On the y-axis, the “Fraction of triggers imaged” represents the fraction of triggers with the given detection
-19
Median Searched Area (square degrees)
10-1
10-2-22
153 Hz sine-Gaussian
3
10
10-1
All Waveforms
White Noise Bursts
10-2-22
Median Searched Area (square degrees)
-18.5
Median Searched Area (square degrees)
Median Searched Area (square degrees)
LSC+Virgo+others: First prompt search for GW transients with EM counterparts
-18.5
Fig. 4. Plots of typical uncertainty region sizes
for the Omega (top) and unconstrained cWB
(bottom) pipelines, as a function of GW strain
amplitude at Earth, for various waveform types.
The “searched area” is the area of the skymap
with a likelihood value greater than the likelihood value at the true source location before the
galaxy catalog is used to further limit the search
region. The solid 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 square degrees. The Omega pipeline performs poorly on
white noise bursts but exceptionally well on
sine-Gaussians because it is designed to identify signals that are well-localized in frequency
space.
3
10
102
10
1
All inspiral waveforms
10-1
10-210
12
14
16
18 20
ρcombined
22
24
26
28
Fig. 5. Plots of uncertainty region sizes for the MBTA pipeline as a
function of combined SNR (ρcombined ). The solid line with symbols
represents the median (50%) performance, while the upper and lower
dashed lines show the 75% and 25% quartile values. The expected detection threshold is around ρcombined ∼ 12.
statistic that have the correct image location included within the
selected tiles. Given a GW trigger, the success rate plotted in
Fig. 6 estimates the odds of choosing the right sky position. In
this figure, note that the “whole skymap” is limited to 160 square
degrees, and so does not always include the true source location.
The thresholds for initiating follow-ups varied with the condition of the interferometers, but was typically around 3.0 for Ω,
η = 3.5 for cWB, and ρcombined = 10 for MBTA. The complex behavior of the Omega efficiency curve is related to the
use of a hybrid detection statistic which utilizes different methods depending on SNR range. Clearly, events with SNR near the
threshold for triggering follow-ups, the most likely scenario for
the first detections, are the most difficult to localize.
Example efficiency curves for the burst simulation are shown
in Fig. 7. The efficiency for each marker on the plot is calculated
A124, page 11 of 15
Fraction of injections imaged with Swift
A&A 539, A124 (2012)
0.8
0.7
0.6
0.5
0.4
1 Tile
2 Tiles
3 Tiles
Whole Skymap
0.3
0.2
0.1
0
0
5
10
15
20
η
25
30
35
40
Fraction of triggers imaged
1
0.9
0.8
0.7
0.6
0.5
0.4
1 Tile
2 Tiles
3 Tiles
Whole Skymap
0.3
0.2
0.1
0
2
2.5
3
3.5
4
Ω statistic
4.5
5
Fraction of triggers imaged
1
0.8
0.7
0.6
0.5
0.4
1 Tile
2 Tiles
3 Tiles
Whole Skymap
0.2
0.1
0
10
12
14
16
18 20 22
ρcombined
24
26
28
30
Fig. 6. Success rates for the tile selection process based on unconstrained cWB (top), Omega (middle), and MBTA (bottom) skymaps. An
injection recovered with the detection statistic shown on the horizontal
axis is considered a success if the correct source location is included in
one of the chosen tiles. Typical thresholds for follow-up are Ω = 3.0,
η = 3.5, and ρcombined = 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.
as the fraction of signals for which the injected location was
successfully imaged, for an hrss 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
per day of livetime;
2. the true source location is included in one of the chosen tiles.
Five tiles are allowed for Swift, three tiles for the QUEST
camera, and one tile for all other telescopes.
A124, page 12 of 15
1
0.9
0.8
0.7
0.6
0.5
0.4
cWB unconstrained
0.3
cWB linear
0.2
Omega
0.1
0
-22
Logical Or
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-1/2
log10(hrss [Hz ])
-19
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1
0.9
0.8
0.7
0.6
0.5
0.4
cWB unconstrained
0.3
cWB linear
0.2
Omega
0.1
0
-22
Logical Or
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-1/2
log10(hrss [Hz ])
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-18.5
Fig. 7. Fractional success as a function of strain at Earth for combinations of the Omega and cWB burst pipelines. Success rates assume
5 pointings for each event for Swift (top) and 3 for QUEST (bottom).
Fractional success is end-to-end from triggering by pipeline to successful pointing, with a threshold for follow-up approximating a FAR of one
per day. The “Logical Or” curve counts a success if either linear cWB
or Omega correctly localized the event, effectively doubling the allowed
number of tiles. Some curves show degraded performance for very loud
signals because the algorithms are tuned to optimize performance for
weaker events close to the detection threshold. Statistical uncertainties
are small with respect to the markers.
0.9
0.3
Fraction of injections imaged with QUEST
Fraction of triggers imaged
1
0.9
Note that efficiencies in this figure do not reach unity even for
loud events primarily due to the difficulty of correctly localizing
GWs in some regions of the sky where the antenna response of
one or more interferometers is poor.
The efficiencies produced with these criteria are upper limits
on what would be detected in a real search. They assume that
the EM transient is very bright, and will always be detected if
the correct sky location is imaged. The quoted efficiencies do not
account for the fact that some chosen tiles will not be observed
due to restrictions from weather, instrument availability, proximity to the Sun or Moon, or the application of a manual veto. The
exact behavior of the efficiency curves will vary depending on
the morphologies of the simulation waveforms selected. Finally,
the chosen GW trigger FAR of one event per day presumes the
false alarm fraction from the EM transient classification pipeline
will be low enough to make a coincidence significant. Many of
these additional complications and their associated impact on efficiency are described in Metzger & Berger (2012).
Nevertheless, these curves provide a measure of the potential
for joint EM/GW searches. If the number of incidental EM transients in the observed fields can be understood and controlled,
then the addition of EM data can effectively increase the search
sensitivity to very weak GW signals. For occasional strong GW
LSC+Virgo+others: First prompt search for GW transients with EM counterparts
Fraction of triggers imaged
1
QUEST (3 tiles)
0.9
Swift (5 tiles)
0.8
TAROT/ROTSE (1 tile)
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
η
25
30
35
40
Fig. 8. Fractional success rate for simulated gravitational wave signals
with (dashed) and without (solid) including calibration uncertainties.
QUEST results assume 3 pointings, Swift results assume 5 pointings
and TAROT/ROTSE results assume 1 pointing.
signals, the plots suggest that only a few pointings of a telescope are enough to image the true location with better than 50%
efficiency.
7.3. Calibration uncertainty
Uncertainty in the calibration of GW detectors (Abadie et al.
2010a; Accadia et al. 2011) may impact the ability to correctly
choose the right fields to observe with EM instruments. To estimate the potential detriment to pointing, we generated a second
set of simulated burst signals, with each signal including some
level of miscalibration corresponding to realistic calibration errors. Before being added to detector noise, each astrophysical
signal was scaled in amplitude by a factor between 0.85 and
1.15, and shifted in time by between −150 and 150 µs. The exact
amplitude and time “jitter” were randomly selected from flat distributions for each signal entering each detector. The bounds of
the distribution of values for the timing and amplitude jitter were
chosen to match preliminary estimates for the LIGO and Virgo
calibration error budgets around 150 Hz for the 2009–2010 run.
Well above this frequency, the actual timing errors are likely less
than this model; the simulation is conservative in this sense.
Some of the results of this simulation, with the cWB algorithm, may be seen in Fig. 8. The success rate is shown for the
entire pipeline, assuming one pointing of a 1.85◦ × 1.85◦ FOV,
three pointings of the QUEST FOV, and five pointings of a Swift
FOV. The curves are shown both with and without the effects
of calibration uncertainty. For the low SNR signals that are the
most likely for first detections, η <
∼ 10, the efficiency is within
a few percent with and without the calibration uncertainty. This
is expected, since at low signal to noise ratio, timing uncertainty
from detector noise is larger than timing uncertainty due to calibration (Fairhurst 2009). However, for louder signals, the ability
to correctly choose the right sky location is seen to be modestly
impacted by the accuracy of the calibration.
8. Summary
Mergers of compact binary systems containing neutron stars, as
well as some other energetic astrophysical events, are expected
to emit observable transients in both the gravitational wave and
electromagnetic channels. Observing populations of joint signals
would likely reveal many details of the GW sources, and could
even constrain cosmological models.
During 2009 and 2010, the LIGO and Virgo collaborations
partnered with a large, heterogeneous group of EM observatories to jointly seek transient signals. X-ray, optical, and radio observatories collected follow-up observations to GW triggers that
were delivered with ∼30 min of latency. Analysis of the multiinstrument data set is currently in progress, and the results of the
search for jointly observed transients will be published at a later
date.
A Monte Carlo study of the GW data analysis algorithms
used in the low latency pipeline demonstrated the ability of the
LIGO/Virgo network to localize transient GW events on the sky.
Localization ability depends strongly on the SNR of the GW
signal; lower SNR signals are more difficult to localize, but are
also the more likely scenario for the first detections. Signals with
SNR near the detection threshold were localized with median
sky areas between 10 and 100 square degrees. After limiting
the search to known galaxies and Milky Way globular clusters
within the detection range of the GW observatories, the simulation shows that the correct location of signals detected near
threshold can be imaged with 30−50% success with three fields
of size 1.85◦ × 1.85◦, for instance. Moreover, the ability to image the source position is seen to be only marginally impacted
by realistic levels of calibration uncertainty.
This search establishes a baseline for low-latency analysis
with the next-generation GW detectors Advanced LIGO and
Advanced Virgo. Installation of these second-generation detectors is already in progress, with observations expected to begin
around 2015. Developing a low-latency response to GW triggers represents the first steps toward solving the many logistical and technical 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 continue to develop over the next few years as the scientific
community prepares to utilize the many opportunities promised
by the impending global network of advanced GW detectors.
Acknowledgements. The authors gratefully acknowledge the support of the
United States National Science Foundation for the construction and operation of the LIGO Laboratory, the Science and Technology Facilities
Council of the United Kingdom, the Max-Planck-Society, and the State of
Niedersachsen/Germany for support of the construction and operation of the
GEO600 detector, and the Italian Istituto Nazionale di Fisica Nucleare and
the French Centre National de la Recherche Scientifique for the construction
and operation of the Virgo detector. The authors also gratefully acknowledge the support of gravitational wave research by these agencies and by the
Australian Research Council, the International Science Linkages program of the
Commonwealth of Australia, the Council of Scientific and Industrial Research of
India, the Istituto Nazionale di Fisica Nucleare of Italy, the Spanish Ministerio
de Educación y Ciencia, the Conselleria d’Economia Hisenda i Innovació of
the Govern de les Illes Balears, the Foundation for Fundamental Research on
Matter supported by The Netherlands Organisation for Scientific Research, the
Polish Ministry of Science and Higher Education, the FOCUS Programme of
Foundation for Polish Science, the Royal Society, the Scottish Funding Council,
the Scottish Universities Physics Alliance, The National Aeronautics and Space
Administration, the Carnegie Trust, the Leverhulme Trust, the David and
Lucile Packard Foundation, the Research Corporation, and the Alfred P. Sloan
Foundation. The authors acknowledge support for TAROT from the French
Ministère des Affaires Étrangères and Ministère de l’Enseignement Supérieur et
de la Recherche. The observations by ROTSE-III were supported by NASA grant
NNX08AV63G and NSF grant PHY-0801007. The work with Swift was partially
supported through a NASA grant/cooperative agreement number NNX09AL61G
to the Massachusetts 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 research project. We thank Joshua S. Bloom for useful discussions on the
rates of PTF transients and their classification. This document has been assigned
LIGO Laboratory document number LIGO-P1000061-v19.
A124, page 13 of 15
A&A 539, A124 (2012)
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LIGO - California Institute of Technology, Pasadena, CA 91125,
USA
California State University Fullerton, Fullerton CA 92831, USA
SUPA, University of Glasgow, Glasgow, G12 8QQ, UK
Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP),
Université de Savoie, CNRS/IN2P3, 74941 Annecy-Le-Vieux,
France
INFN, Sezione di Napoli, Complesso Universitario di Monte
S.Angelo, 80126 Napoli, Italy
Università di Napoli “Federico II”, Complesso Universitario di
Monte S.Angelo, 80126 Napoli, Italy
Università di Salerno, Fisciano, 84084 Salerno, Italy
LIGO - Livingston Observatory, Livingston, LA 70754, USA
Albert-Einstein-Institut,
Max-Planck-Institut
für
Gravitationsphysik, 30167 Hannover, Germany
Leibniz Universität Hannover, 30167 Hannover, Germany
Nikhef, Science Park, Amsterdam, The Netherlands
LSC+Virgo+others: First prompt search for GW transients with EM counterparts
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VU University Amsterdam, De Boelelaan 1081, 1081 HV
Amsterdam, The Netherlands
University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
Stanford University, Stanford, CA 94305, USA
University of Florida, Gainesville, FL 32611, USA
Louisiana State University, Baton Rouge, LA 70803, USA
University of Birmingham, Birmingham, B15 2TT, UK
INFN, Sezione di Roma, 00185 Roma, Italy
Università “La Sapienza”, 00185 Roma, Italy
LIGO - Hanford Observatory, Richland, WA 99352, USA
Albert-Einstein-Institut,
Max-Planck-Institut
für
Gravitationsphysik, 14476 Golm, Germany
Montana State University, Bozeman, MT 59717, USA
European Gravitational Observatory (EGO), 56021 Cascina (PI),
Italy
Syracuse University, Syracuse, NY 13244, USA
University of Western Australia, Crawley, WA 6009, Australia
LIGO - Massachusetts Institute of Technology, Cambridge, MA
02139, USA
APC, AstroParticule et Cosmologie, Université Paris Diderot,
CNRS/IN2P3, CEA/Irfu, Observatoire de Paris, Sorbonne Paris
Cité, 10 rue Alice Domon et Léonie Duquet, 75205 Paris Cedex
13, France
Columbia University, New York, NY 10027, USA
INFN, Sezione di Pisa, 56127 Pisa, Italy
Università di Pisa, 56127 Pisa, Italy
Università di Siena, 53100 Siena, Italy
The University of Texas at Brownsville, Brownsville, TX 78520,
USA
San Jose State University, San Jose, CA 95192, USA
Moscow State University, Moscow 119992, Russia
LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
ESPCI, CNRS, 75005 Paris, France
NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA
The Pennsylvania State University, University Park, PA 16802,
USA
Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte
d’Azur, 06304 Nice, France
Institut de Physique de Rennes, CNRS, Université de Rennes 1,
35042 Rennes, France
Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, 69622
Villeurbanne, Lyon, France
Washington State University, Pullman, WA 99164, USA
INFN, Sezione di Perugia, 06123 Perugia,Italy
Università di Perugia, 06123 Perugia,Italy
INFN, Sezione di Firenze, 50019 Sesto Fiorentino, Italy
Università degli Studi di Urbino “Carlo Bo”, 61029 Urbino, Italy
University of Oregon, Eugene, OR 97403, USA
Laboratoire Kastler Brossel, ENS, CNRS, UPMC, Université Pierre
et Marie Curie, 4 Place Jussieu, 75005 Paris, France
Rutherford Appleton Laboratory, HSIC, Chilton, Didcot, Oxon
OX11 0QX, UK
IM-PAN 00-956 Warsaw, Poland
Astronomical Observatory Warsaw University 00-478 Warsaw,
Poland
CAMK-PAN 00-716 Warsaw, Poland
Białystok University 15-424 Białystok, Poland
NCBJ 05-400 Świerk-Otwock, Poland
Institute of Astronomy 65-265 Zielona Góra, Poland
University of Maryland, College Park, MD 20742 USA
Universitat de les Illes Balears, 07122 Palma de Mallorca, Spain
University of Massachusetts - Amherst, Amherst, MA 01003, USA
Canadian Institute for Theoretical Astrophysics, University of
Toronto, Toronto, Ontario, M5S 3H8, Canada
Tsinghua University, Beijing 100084, PR China
University of Michigan, Ann Arbor, MI 48109, USA
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Charles Sturt University, Wagga Wagga, NSW 2678, Australia
Caltech-CaRT, Pasadena, CA 91125, USA
INFN, Sezione di Genova; 16146 Genova, Italy
Pusan National University, Busan 609-735, Korea
Carleton College, Northfield, MN 55057, USA
Australian National University, Canberra, ACT 0200, Australia
The University of Melbourne, Parkville, VIC 3010, Australia
Cardiff University, Cardiff, CF24 3AA, UK
INFN, Sezione di Roma Tor Vergata, 00133 Roma, Italy
Università di Roma Tor Vergata, 00133 Roma, Italy
Università dell’Aquila, 67100 L’Aquila, Italy
University of Salerno, 84084 Fisciano (Salerno), Italy and INFN
(Sezione di Napoli), Italy
The University of Sheffield, Sheffield S10 2TN, UK
WIGNER RCP, RMKI, H-1121 Budapest, Konkoly Thege Miklós
út 29-33, Hungary
INFN, Gruppo Collegato di Trento, 38050 Povo, Trento, Italy
Università di Trento, 38050 Povo, Trento, Italy
INFN, Sezione di Padova, 35131 Padova, Italy
Inter-University Centre for Astronomy and Astrophysics, Pune
411007, India
University of Minnesota, Minneapolis, MN 55455, USA
California Institute of Technology, Pasadena, CA 91125, USA
Northwestern University, Evanston, IL 60208, USA
The University of Texas at Austin, Austin, TX 78712, USA
Rochester Institute of Technology, Rochester, NY 14623, USA
Eötvös Loránd University, Budapest, 1117 Hungary
University of Adelaide, Adelaide, SA 5005, Australia
University of Szeged, 6720 Szeged, Dóm tér 9, Hungary
Embry-Riddle Aeronautical University, Prescott, AZ 86301 USA
National Institute for Mathematical Sciences, Daejeon 305-390,
Korea
Perimeter Institute for Theoretical Physics, Ontario, N2L 2Y5,
Canada
National Astronomical Observatory of Japan, Tokyo 181-8588,
Japan
Korea Institute of Science and Technology Information, Daejeon
305-806, Korea
University of Southampton, Southampton, SO17 1BJ, UK
Institute of Applied Physics, Nizhny Novgorod, 603950, Russia
Lund Observatory, Box 43, SE-221 00, Lund, Sweden
Hanyang University, Seoul 133-791, Korea
Seoul National University, Seoul 151-742, Korea
University of Strathclyde, Glasgow, G1 1XQ, UK
Southern University and A&M College, Baton Rouge, LA 70813,
USA
University of Rochester, Rochester, NY 14627, USA
Hobart and William Smith Colleges, Geneva, NY 14456, USA
University of Sannio at Benevento, 82100 Benevento, Italy and
INFN (Sezione di Napoli), Italy
Louisiana Tech University, Ruston, LA 71272, USA
McNeese State University, Lake Charles, LA 70609 USA
University of Washington, Seattle, WA, 98195-4290, USA
Andrews University, Berrien Springs, MI 49104 USA
Trinity University, San Antonio, TX 78212, USA
Southeastern Louisiana University, Hammond, LA 70402, USA
Institut de Recherche en Astrophysique et Planetologie (IRAP), 14
Avenue Edouard Belin, 31400 Toulouse, France
NASA Einstein Fellow
“Pi of the Sky” and the Andrzej Soltan Institute for Nuclear Studies,
Hoza 69, 00-681 Warsaw, Poland
Jodrell Bank Center for Astrophysics, University of Manchester
Liverpool John Moores University, Liverpool L3 2AJ, UK
Astronomical Institute “Anton Pannekoek”, University of
Amsterdam, 1090 GE Amsterdam, The Netherlands
A124, page 15 of 15
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