CLASSICAL AND QUANTUM GRAVITY

Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914
Abbott BP, Abbott R, Abbott TD, Abernathy MR, Acernese F, Ackley K, Adamo M, Adams C, Adams T, Addesso P, Adhikari RX, Adya VB, Affeldt C, Agathos M, Agatsuma K, Aggarwal N, Aguiar OD, Aiello L, Ain A, Ajith P, Allen B, Allocca A, Altin PA, Anderson SB, Anderson WG, Arai K, Araya MC, Arceneaux CC, Areeda JS, Arnaud N, Arun KG, Ascenzi S, Ashton G, Ast M, Aston SM, Astone P, Aufmuth P, Aulbert C, Babak S, Bacon P, Bader MKM, Baker PT, Baldaccini F, Ballardin G, Ballmer SW, Barayoga JC, Barclay SE, Barish BC, Barker D, Barone F, Barr B, Barsotti L, Barsuglia M, Barta D, Bartlett J, Bartos I, Bassiri R, Basti A, Batch JC, Baune C, Bavigadda V, Bazzan M, Behnke B, Bejger M, Bell AS, Bell CJ, Berger BK, Bergman J, Bergmann G, Berry CPL, Bersanetti D, Bertolini A, Betzwieser J, Bhagwat S, Bhandare R, Bilenko IA, Billingsley G, Birch J, Birney R, Biscans S, Bisht A, Bitossi M, Biwer C, Bizouard MA, Blackburn JK, Blackburn L, Blair CD, Blair DG, Blair RM, Bloemen S, Bock O, Bodiya TP, Boer M, Bogaert G, Bogan C, Bohe A, Bojtos P, Bond C, Bondu F, Bonnand R, Boom BA, Bork R, Boschi V, Bose S, Bouffanais Y, Bozzi A, Bradaschia C, Brady PR, Braginsky VB, Branchesi M, Brau JE, Briant T, Brillet A, Brinkmann M, Brisson V, Brockill P, Brooks AF, Brown DA, Brown DD, Brown NM, Buchanan CC, Buikema A, Bulik T, Bulten HJ, Buonanno A, Buskulic D, Buy C, Byer RL, Cadonati L, Cagnoli G, Cahillane C, Bustillo JC, Callister T, Calloni E, Camp JB, Cannon KC, Cao J, Capano CD, Capocasa E, Carbognani F, Caride S, Diaz JC, Casentini C, Caudill S, Cavaglià M, Cavalier F, Cavalieri R, Cella G, Cepeda CB, Baiardi LC, Cerretani G, Cesarini E, Chakraborty R, Chalermsongsak T, Chamberlin SJ, Chan M, Chao S, Charlton P, Chassande-Mottin E, Chatterji S, Chen HY, Chen Y, Cheng C, Chincarini A, Chiummo A, Cho HS, Cho M, Chow JH, Christensen N, Chu Q, Chua S, Chung S, Ciani G, Clara F, Clark JA, Cleva F, Coccia E, Cohadon PF, Colla A, Collette CG, Cominsky L, Constancio M, Conte A, Conti L, Cook D, Corbitt TR, Cornish N, Corsi A, Cortese S, Costa CA, Coughlin MW, Coughlin SB, Coulon JP, Countryman ST, Couvares P, Cowan EE, Coward DM, Cowart MJ, Coyne DC, Coyne R, Craig K, Creighton JDE, Cripe J, Crowder SG, Cumming A, Cunningham L, Cuoco E, Canton TD, Danilishin SL, D'Antonio S, Danzmann K, Darman NS, Dattilo V, Dave I, Daveloza HP, Davier M, Davies GS, Daw EJ, Day R, DeBra D, Debreczeni G, Degallaix J, Laurentis M, Deléglise S, Pozzo WD, Denker T, Dent T, Dereli H, Dergachev V, DeRosa RT, Rosa R, DeSalvo R, Dhurandhar S, Díaz MC, Fiore LD, Giovanni MD, Lieto AD, Pace SD, Palma ID, Virgilio AD, Dojcinoski G, Dolique V, Donovan F, Dooley KL, Doravari S, Douglas R, Downes TP, Drago M, Drever RWP, Driggers JC, Du Z, Ducrot M, Dwyer SE, Edo TB, Edwards MC, Effler A, Eggenstein HB, Ehrens P, Eichholz J, Eikenberry SS, Engels W, Essick RC, Etzel T, Evans M, Evans TM, Everett R, Factourovich M, Fafone V, Fair H, Fairhurst S, Fan X, Fang Q, Farinon S, Farr B, Farr WM, Favata M, Fays M, Fehrmann H, Fejer MM, Ferrante I, Ferreira EC, Ferrini F, Fidecaro F, Fiori I, Fiorucci D, Fisher RP, Flaminio R, Fletcher M, Fournier JD, Franco S, Frasca S, Frasconi F, Frei Z, Freise A, Frey R, Frey V, Fricke TT, Fritschel P, Frolov VV, Fulda P, Fyffe M, Gabbard HAG, Gair JR, Gammaitoni L, Gaonkar SG, Garufi F, Gatto A, Gaur G, Gehrels N, Gemme G, Gendre B, Genin E, Gennai A, George J, Gergely L, Germain V, Ghosh A, Ghosh S, Giaime JA, Giardina KD, Giazotto A, Gill K, Glaefke A, Goetz E, Goetz R, Gondan L, González G, Gonzalez Castro JM, Gopakumar A, Gordon NA, Gorodetsky ML, Gossan SE, Gosselin M, Gouaty R, Graef C, Graff PB, Granata M, Grant A, Gras S, Gray C, Greco G, Green AC, Groot P, Grote H, Grunewald S, Guidi GM, Guo X, Gupta A, Gupta MK, Gushwa KE, Gustafson EK, Gustafson R, Hacker JJ, Hall BR, Hall ED, Hammond G, Haney M, Hanke MM, Hanks J, Hanna C, Hannam MD, Hanson J, Hardwick T, Harms J, Harry GM, Harry IW, Hart MJ, Hartman MT, Haster CJ, Haughian K, Heidmann A, Heintze MC, Heitmann H, Hello P, Hemming G, Hendry M, Heng IS, Hennig J, Heptonstall AW, Heurs M, Hild S, Hoak D, Hodge KA, Hofman D, Hollitt SE, Holt K, Holz DE, Hopkins P, Hosken DJ, Hough J, Houston EA, Howell EJ, Hu YM, Huang S, Huerta EA, Huet D, Hughey B, Husa S, Huttner SH, Huynh-Dinh T, Idrisy A, Indik N, Ingram DR, Inta R, Isa HN, Isac JM, Isi M, Islas G, Isogai T, Iyer BR, Izumi K, Jacqmin T, Jang H, Jani K, Jaranowski P, Jawahar S, Jiménez-Forteza F, Johnson WW, Jones DI, Jones R, Jonker RJG, Ju L, Haris K, Kalaghatgi CV, Kalogera V, Kandhasamy S, Kang G, Kanner JB, Karki S, Kasprzack M, Katsavounidis E, Katzman W, Kaufer S, Kaur T, Kawabe K, Kawazoe F, Kéfélian F, Kehl MS, Keitel D, Kelley DB, Kells W, Kennedy R, Key JS, Khalaidovski A, Khalili FY, Khan I, Khan S, Khan Z, Khazanov EA, Kijbunchoo N, Kim C, Kim J, Kim K, Nam-Gyu K, Kim N, Kim YM, King EJ, King PJ, Kinzel DL, Kissel JS, Kleybolte L, Klimenko S, Koehlenbeck SM, Kokeyama K, Koley S, Kondrashov V, Kontos A, Korobko M, Korth WZ, Kowalska I, Kozak DB, Kringel V, Krishnan B, Królak A, Krueger C, Kuehn G, Kumar P, Kuo L, Kutynia A, Lackey BD, Landry M, Lange J, Lantz B, Lasky PD, Lazzarini A, Lazzaro C, Leaci P, Leavey S, Lebigot EO, Lee CH, Lee HK, Lee HM, Lee K, Lenon A, Leonardi M, Leong JR, Leroy N, Letendre N, Levin Y, Levine BM, Li TGF, Libson A, Littenberg TB, Lockerbie NA, Logue J, Lombardi AL, Lord JE, Lorenzini M, Loriette V, Lormand M, Losurdo G, Lough JD, Lück H, Lundgren AP, Luo J, Lynch R, Ma Y, MacDonald T, Machenschalk B, MacInnis M, Macleod DM, Magaña-Sandoval F, Magee RM, Mageswaran M, Majorana E, Maksimovic I, Malvezzi V, Man N, Mandel I, Mandic V, Mangano V, Mansell GL, Manske M, Mantovani M, Marchesoni F, Marion F, Márka S, Márka Z, Markosyan AS, Maros E, Martelli F, Martellini L, Martin IW, Martin RM, Martynov DV, Marx JN, Mason K, Masserot A, Massinger TJ, Masso-Reid M, Matichard F, Matone L, Mavalvala N, Mazumder N, Mazzolo G, McCarthy R, McClelland DE, McCormick S, McGuire SC, McIntyre G, McIver J, McManus DJ, McWilliams ST, Meacher D, Meadors GD, Meidam J, Melatos A, Mendell G, Mendoza-Gandara D, Mercer RA, Merilh E, Merzougui M, Meshkov S, Messenger C, Messick C, Meyers PM, Mezzani F, Miao H, Michel C, Middleton H, Mikhailov EE, Milano L, Miller J, Millhouse M, Minenkov Y, Ming J, Mirshekari S, Mishra C, Mitra S, Mitrofanov VP, Mitselmakher G, Mittleman R, Moggi A, Mohan M, Mohapatra SRP, Montani M, Moore BC, Moore CJ, Moraru D, Moreno G, Morriss SR, Mossavi K, Mours B, Mow-Lowry CM, Mueller CL, Mueller G, Muir AW, Mukherjee A, Mukherjee D, Mukherjee S, Mukund N, Mullavey A, Munch J, Murphy DJ, Murray PG, Mytidis A, Nardecchia I, Naticchioni L, Nayak RK, Necula V, Nedkova K, Nelemans G, Neri M, Neunzert A, Newton G, Nguyen TT, Nielsen AB, Nissanke S, Nitz A, Nocera F, Nolting D, Normandin ME, Nuttall LK, Oberling J, Ochsner E, O'Dell J, Oelker E, Ogin GH, Oh JJ, Oh SH, Ohme F, Oliver M, Oppermann P, Oram RJ, O'Reilly B, O'Shaughnessy R, Ottaway DJ, Ottens RS, Overmier H, Owen BJ, Pai A, Pai SA, Palamos JR, Palashov O, Palomba C, Pal-Singh A, Pan H, Pankow C, Pannarale F, Pant BC, Paoletti F, Paoli A, Papa MA, Paris HR, Parker W, Pascucci D, Pasqualetti A, Passaquieti R, Passuello D, Patricelli B, Patrick Z, Pearlstone BL, Pedraza M, Pedurand R, Pekowsky L, Pele A, Penn S, Perreca A, Phelps M, Piccinni O, Pichot M, Piergiovanni F, Pierro V, Pillant G, Pinard L, Pinto IM, Pitkin M, Poggiani R, Popolizio P, Post A, Powell J, Prasad J, Predoi V, Premachandra SS, Prestegard T, Price LR, Prijatelj M, Principe M, Privitera S, Prodi GA, Prokhorov L, Puncken O, Punturo M, Puppo P, Pürrer M, Qi H, Qin J, Quetschke V, Quintero EA, Quitzow-James R, Raab FJ, Rabeling DS, Radkins H, Raffai P, Raja S, Rakhmanov M, Rapagnani P, Raymond V, Razzano M, Re V, Read J, Reed CM, Regimbau T, Rei L, Reid S, Reitze DH, Rew H, Reyes SD, Ricci F, Riles K, Robertson NA, Robie R, Robinet F, Rocchi A, Rolland L, Rollins JG, Roma VJ, Romano R, Romanov G, Romie JH, 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AL, Summerscales TZ, Sun L, Sutton PJ, Swinkels BL, Szczepańczyk MJ, Tacca M, Talukder D, Tanner DB, Tápai M, Tarabrin SP, Taracchini A, Taylor R, Theeg T, Thirugnanasambandam MP, Thomas EG, Thomas M, Thomas P, Thorne KA, Thorne KS, Thrane E, Tiwari S, Tiwari V, Tokmakov KV, Tomlinson C, Tonelli M, Torres CV, Torrie CI, Töyrä D, Travasso F, Traylor G, Trifirò D, Tringali MC, Trozzo L, Tse M, Turconi M, Tuyenbayev D, Ugolini D, Unnikrishnan CS, Urban AL, Usman SA, Vahlbruch H, Vajente G, Valdes G, van Bakel N, van Beuzekom M, van den Brand JFJ, Van Den Broeck C, Vander-Hyde DC, van der Schaaf L, van Heijningen JV, van Veggel AA, Vardaro M, Vass S, Vasúth M, Vaulin R, Vecchio A, Vedovato G, Veitch J, Veitch PJ, Venkateswara K, Verkindt D, Vetrano F, Viceré A, Vinciguerra S, Vine DJ, Vinet JY, Vitale S, Vo T, Vocca H, Vorvick C, Voss D, Vousden WD, Vyatchanin SP, Wade AR, Wade LE, Wade M, Walker M, Wallace L, Walsh S, Wang G, Wang H, Wang M, Wang X, Wang Y, Ward RL, Warner J, Was M, Weaver B, Wei LW, Weinert M, Weinstein AJ, Weiss R, Welborn T, Wen L, Weßels P, Westphal T, Wette K, Whelan JT, Whitcomb S, White DJ, Whiting BF, Williams RD, Williamson AR, Willis JL, Willke B, Wimmer MH, Winkler W, Wipf CC, Wittel H, Woan G, Worden J, Wright JL, Wu G, Yablon J, Yam W, Yamamoto H, Yancey CC, Yap MJ, Yu H, Yvert M, Zadrożny A, Zangrando L, Zanolin M, Zendri JP, Zevin M, Zhang F, Zhang L, Zhang M, Zhang Y, Zhao C, Zhou M, Zhou Z, Zhu XJ, Zotov N, Zucker ME, Zuraw SE and Zweizig J
On September 14, 2015, a gravitational wave signal from a coalescing black hole binary system was observed by the Advanced LIGO detectors. This paper describes the transient noise backgrounds used to determine the significance of the event (designated GW150914) and presents the results of investigations into potential correlated or uncorrelated sources of transient noise in the detectors around the time of the event. The detectors were operating nominally at the time of GW150914. We have ruled out environmental influences and non-Gaussian instrument noise at either LIGO detector as the cause of the observed gravitational wave signal.
Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science
Zevin M, Coughlin S, Bahaadini S, Besler E, Rohani N, Allen S, Cabero M, Crowston K, Katsaggelos AK, Larson SL, Lee TK, Lintott C, Littenberg TB, Lundgren A, Østerlund C, Smith JR, Trouille L and Kalogera V
With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as , which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.