key: cord-0306431-4vfxf4v5
authors: Collaboration, The LIGO Scientific; Collaboration, the Virgo; Abbott, the KAGRA Collaboration R.; Abbott, T. D.; Acernese, F.; Ackley, K.; Adams, C.; Adhikari, N.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agarwal, D.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Akutsu, T.; Albanesi, S.; Allocca, A.; Altin, P. A.; Amato, A.; Anand, C.; Anand, S.; Ananyeva, A.; Anderson, S. B.; Anderson, W. G.; Ando, M.; Andrade, T.; Andres, N.; Andri'c, T.; Angelova, S. V.; Ansoldi, S.; Antelis, J. M.; Antier, S.; Appert, S.; Arai, Koji; Arai, Koya; Arai, Y.; Araki, S.; Araya, A.; Araya, M. C.; Areeda, J. S.; Arene, M.; Aritomi, N.; Arnaud, N.; Aronson, S. M.; Arun, K. G.; Asada, H.; Asali, Y.; Ashton, G.; Aso, Y.; Assiduo, M.; Aston, S. M.; Astone, P.; Aubin, F.; Austin, C.; Babak, S.; Badaracco, F.; Bader, M. K. M.; Badger, C.; Bae, S.; Bae, Y.; Baer, A. M.; Bagnasco, S.; Bai, Y.; Baiotti, L.; Baird, J.; Bajpai, R.; Ball, M.; Ballardin, G.; Ballmer, S. W.; Balsamo, A.; Baltus, G.; Banagiri, S.; Bankar, D.; Barayoga, J. C.; Barbieri, C.; Barish, B. C.; Barker, D.; Barneo, P.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Barton, M. A.; Bartos, I.; Bassiri, R.; Basti, A.; Bawaj, M.; Bayley, J. C.; Baylor, A. C.; Bazzan, M.; B'ecsy, B.; Bedakihale, V. M.; Bejger, M.; Belahcene, I.; Benedetto, V.; Beniwal, D.; Bennett, T. F.; Bentley, J. D.; BenYaala, M.; Bergamin, F.; Berger, B. K.; Bernuzzi, S.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Beveridge, D.; Bhandare, R.; Bhardwaj, U.; Bhattacharjee, D.; Bhaumik, S.; Bilenko, I. A.; Billingsley, G.; Bini, S.; Birney, R.; Birnholtz, O.; Biscans, S.; Bischi, M.; Biscoveanu, S.; Bisht, A.; Biswas, B.; Bitossi, M.; Bizouard, M.-A.; Blackburn, J. K.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bobba, F.; Bode, N.; Boer, M.; Bogaert, G.; Boldrini, M.; Bonavena, L. D.; Bondu, F.; Bonilla, E.; Bonnand, R.; Booker, P.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, N.; Bose, S.; Bossilkov, V.; Boudart, V.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Bramley, A.; Branch, A.; Branchesi, M.; Brau, J. E.; Breschi, M.; Briant, T.; Briggs, J. H.; Brillet, A.; Brinkmann, M.; Brockill, P.; Brooks, A. F.; Brooks, J.; Brown, D. D.; Brunett, S.; Bruno, G.; Bruntz, R.; Bryant, J.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buscicchio, R.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Bustillo, J. Calder'on; Callaghan, J. D.; Callister, T. A.; Calloni, E.; Cameron, J.; Camp, J. B.; Canepa, M.; Canevarolo, S.; Cannavacciuolo, M.; Cannon, K. C.; Cao, H.; Cao, Z.; Capocasa, E.; Capote, E.; Carapella, G.; Carbognani, F.; Carlin, J. B.; Carney, M. F.; Carpinelli, M.; Carrillo, G.; Carullo, G.; Carver, T. L.; Diaz, J. Casanueva; Casentini, C.; Castaldi, G.; Caudill, S.; Cavaglia, M.; Cavalier, F.; Cavalieri, R.; Ceasar, M.; Cella, G.; Cerd'a-Dur'an, P.; Cesarini, E.; Chaibi, W.; Chakravarti, K.; Subrahmanya, S. Chalathadka; Champion, E.; Chan, C.-H.; Chan, C.; Chan, C. L.; Chan, K.; Chan, M.; Chandra, K.; Chanial, P.; Chao, S.; Charlton, P.; Chase, E. A.; Chassande-Mottin, E.; Chatterjee, C.; Chatterjee, Debarati; Chatterjee, Deep; Chaturvedi, M.; Chaty, S.; Chen, C.; Chen, H. Y.; Chen, J.; Chen, K.; Chen, X.; Chen, Y.-B.; Chen, Y.-R.; Chen, Z.; Cheng, H.; Cheong, C. K.; Cheung, H. Y.; Chia, H. Y.; Chiadini, F.; Chiang, C-Y.; Chiarini, G.; Chierici, R.; Chincarini, A.; Chiofalo, M. L.; Chiummo, A.; Cho, G.; Cho, H. S.; Choudhary, R. K.; Choudhary, S.; Christensen, N.; Chu, H.; Chu, Q.; Chu, Y-K.; Chua, S.; Chung, K. W.; Ciani, G.; Ciecielag, P.; Cie'slar, M.; Cifaldi, M.; Ciobanu, A. A.; Ciolfi, R.; Cipriano, F.; Cirone, A.; Clara, F.; Clark, E. N.; Clark, J. A.; Clarke, L.; Clearwater, P.; Clesse, S.; Cleva, F.; Coccia, E.; Codazzo, E.; Cohadon, P.-F.; Cohen, D. E.; Cohen, L.; Colleoni, M.; Collette, C. G.; Colombo, A.; Colpi, M.; Compton, C. M.; Constancio, M.; Conti, L.; Cooper, S. J.; Corban, P.; Corbitt, T. R.; Cordero-Carri'on, I.; Corezzi, S.; Corley, K. R.; Cornish, N.; Corre, D.; Corsi, A.; Cortese, S.; Costa, C. A.; Cotesta, R.; Coughlin, M. W.; Coulon, J.-P.; Countryman, S. T.; Cousins, B.; Couvares, P.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Creighton, J. D. E.; Creighton, T. D.; Criswell, A. W.; Croquette, M.; Crowder, S. G.; Cudell, J. R.; Cullen, T. J.; Cumming, A.; Cummings, R.; Cunningham, L.; Cuoco, E.; Curylo, M.; Dabadie, P.; Canton, T. Dal; Dall'Osso, S.; D'alya, G.; Dana, A.; DaneshgaranBajastani, L. M.; D'Angelo, B.; Danilishin, S.; D'Antonio, S.; Danzmann, K.; Darsow-Fromm, C.; Dasgupta, A.; Datrier, L. E. H.; Datta, S.; Dattilo, V.; Dave, I.; Davier, M.; Davies, G. S.; Davis, D.; Davis, M. C.; Daw, E. J.; Dean, R.; DeBra, D.; Deenadayalan, M.; Degallaix, J.; Laurentis, M. De; Del'eglise, S.; Favero, V. Del; Lillo, F. De; Lillo, N. De; Pozzo, W. Del; DeMarchi, L. M.; Matteis, F. De; D'Emilio, V.; Demos, N.; Dent, T.; Depasse, A.; Pietri, R. De; Rosa, R. De; Rossi, C. De; DeSalvo, R.; Simone, R. De; Dhurandhar, S.; D'iaz, M. C.; Diaz-Ortiz, M.; Didio, N. A.; Dietrich, T.; Fiore, L. Di; Fronzo, C. Di; Giorgio, C. Di; Giovanni, F. Di; Giovanni, M. Di; Girolamo, T. Di; Lieto, A. Di; Ding, B.; Pace, S. Di; Palma, I. Di; Renzo, F. Di; Divakarla, A. K.; Dmitriev, A.; Doctor, Z.; D'Onofrio, L.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorrington, I.; Drago, M.; Driggers, J. C.; Drori, Y.; Ducoin, J.-G.; Dupej, P.; Durante, O.; D'Urso, D.; Duverne, P.-A.; Dwyer, S. E.; Eassa, C.; Easter, P. J.; Ebersold, M.; Eckhardt, T.; Eddolls, G.; Edelman, B.; Edo, T. B.; Edy, O.; Effler, A.; Eguchi, S.; Eichholz, J.; Eikenberry, S. S.; Eisenmann, M.; Eisenstein, R. A.; Ejlli, A.; Engelby, E.; Enomoto, Y.; Errico, L.; Essick, R. C.; Estell'es, H.; Estevez, D.; Etienne, Z.; Etzel, T.; Evans, M.; Evans, T. M.; Ewing, B. E.; Fafone, V.; Fair, H.; Fairhurst, S.; Farah, A. M.; Farinon, S.; Farr, B.; Farr, W. M.; Farrow, N. W.; Fauchon-Jones, E. J.; Favaro, G.; Favata, M.; Fays, M.; Fazio, M.; Feicht, J.; Fejer, M. M.; Fenyvesi, E.; Ferguson, D. L.; Fernandez-Galiana, A.; Ferrante, I.; Ferreira, T. A.; Fidecaro, F.; Figura, P.; Fiori, I.; Fishbach, M.; Fisher, R. P.; Fittipaldi, R.; Fiumara, V.; Flaminio, R.; Floden, E.; Fong, H.; Font, J. A.; Fornal, B.; Forsyth, P. W. F.; Franke, A.; Frasca, S.; Frasconi, F.; Frederick, C.; Freed, J. P.; Frei, Z.; Freise, A.; Frey, R.; Fritschel, P.; Frolov, V. V.; Fronz'e, G. G.; Fujii, Y.; Fujikawa, Y.; Fukunaga, M.; Fukushima, M.; Fulda, P.; Fyffe, M.; Gabbard, H. A.; Gadre, B. U.; Gair, J. R.; Gais, J.; Galaudage, S.; Gamba, R.; Ganapathy, D.; Ganguly, A.; Gao, D.; Gaonkar, S. G.; Garaventa, B.; Garc'ia-N'unez, C.; Garc'ia-Quir'os, C.; Garufi, F.; Gateley, B.; Gaudio, S.; Gayathri, V.; Ge, G.-G.; Gemme, G.; Gennai, A.; George, J.; Gerberding, O.; Gergely, L.; Gewecke, P.; Ghonge, S.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, Shaon; Ghosh, Shrobana; Giacomazzo, B.; Giacoppo, L.; Giaime, J. A.; Giardina, K. D.; Gibson, D. R.; Gier, C.; Giesler, M.; Giri, P.; Gissi, F.; Glanzer, J.; Gleckl, A. E.; Godwin, P.; Goetz, E.; Goetz, R.; Gohlke, N.; Goncharov, B.; Gonz'alez, G.; Gopakumar, A.; Gosselin, M.; Gouaty, R.; Gould, D. W.; Grace, B.; Grado, A.; Granata, M.; Granata, V.; Grant, A.; Gras, S.; Grassia, P.; Gray, C.; Gray, R.; Greco, G.; Green, A. C.; Green, R.; Gretarsson, A. M.; Gretarsson, E. M.; Griffith, D.; Griffiths, W.; Griggs, H. L.; Grignani, G.; Grimaldi, A.; Grimm, S. J.; Grote, H.; Grunewald, S.; Gruning, P.; Guerra, D.; Guidi, G. M.; Guimaraes, A. R.; Guix'e, G.; Gulati, H. K.; Guo, H.-K.; Guo, Y.; Gupta, Anchal; Gupta, Anuradha; Gupta, P.; Gustafson, E. K.; Gustafson, R.; Guzman, F.; Ha, S.; Haegel, L.; Hagiwara, A.; Haino, S.; Halim, O.; Hall, E. D.; Hamilton, E. Z.; Hammond, G.; Han, W.-B.; Haney, M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hannuksela, O.; Hansen, H.; Hansen, T. J.; Hanson, J.; Harder, T.; Hardwick, T.; Haris, K.; Harms, J.; Harry, G. M.; Harry, I. W.; Hartwig, D.; Hasegawa, K.; Haskell, B.; Hasskew, R. K.; Haster, C.-J.; Hattori, K.; Haughian, K.; Hayakawa, H.; Hayama, K.; Hayes, F. J.; Healy, J.; Heidmann, A.; Heidt, A.; Heintze, M. C.; Heinze, J.; Heinzel, J.; Heitmann, H.; Hellman, F.; Hello, P.; Helmling-Cornell, A. F.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennes, E.; Hennig, J.; Hennig, M. H.; Hernandez, A. G.; Vivanco, F. Hernandez; Heurs, M.; Hild, S.; Hill, P.; Himemoto, Y.; Hines, A. S.; Hiranuma, Y.; Hirata, N.; Hirose, E.; Hochheim, S.; Hofman, D.; Hohmann, J. N.; Holcomb, D. G.; Holland, N. A.; Hollows, I. J.; Holmes, Z. J.; Holt, K.; Holz, D. E.; Hong, Z.; Hopkins, P.; Hough, J.; Hourihane, S.; Howell, E. J.; Hoy, C. G.; Hoyland, D.; Hreibi, A.; Hsieh, B-H.; Hsu, Y.; Huang, G-Z.; Huang, H-Y.; Huang, P.; Huang, Y-C.; Huang, Y.-J.; Huang, Y.; Hubner, M. T.; Huddart, A. D.; Hughey, B.; Hui, D. C. Y.; Hui, V.; Husa, S.; Huttner, S. H.; Huxford, R.; Huynh-Dinh, T.; Ide, S.; Idzkowski, B.; Iess, A.; Ikenoue, B.; Imam, S.; Inayoshi, K.; Ingram, C.; Inoue, Y.; Ioka, K.; Isi, M.; Isleif, K.; Ito, K.; Itoh, Y.; Iyer, B. R.; Izumi, K.; JaberianHamedan, V.; Jacqmin, T.; Jadhav, S. J.; Jadhav, S. P.; James, A. L.; Jan, A. Z.; Jani, K.; Janquart, J.; Janssens, K.; Janthalur, N. N.; Jaranowski, P.; Jariwala, D.; Jaume, R.; Jenkins, A. C.; Jenner, K.; Jeon, C.; Jeunon, M.; Jia, W.; Jin, H.-B.; Johns, G. R.; Jones, A. W.; Jones, D. I.; Jones, J. D.; Jones, P.; Jones, R.; Jonker, R. J. G.; Ju, L.; Jung, P.; Jung, k.; Junker, J.; Juste, V.; Kaihotsu, K.; Kajita, T.; Kakizaki, M.; Kalaghatgi, C. V.; Kalogera, V.; Kamai, B.; Kamiizumi, M.; Kanda, N.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kao, Y.; Kapadia, S. J.; Kapasi, D. P.; Karat, S.; Karathanasis, C.; Karki, S.; Kashyap, R.; Kasprzack, M.; Kastaun, W.; Katsanevas, S.; Katsavounidis, E.; Katzman, W.; Kaur, T.; Kawabe, K.; Kawaguchi, K.; Kawai, N.; Kawasaki, T.; K'ef'elian, F.; Keitel, D.; Key, J. S.; Khadka, S.; Khalili, F. Y.; Khan, S.; Khazanov, E. A.; Khetan, N.; Khursheed, M.; Kijbunchoo, N.; Kim, C.; Kim, J. C.; Kim, J.; Kim, K.; Kim, W. S.; Kim, Y.-M.; Kimball, C.; Kimura, N.; Kinley-Hanlon, M.; Kirchhoff, R.; Kissel, J. S.; Kita, N.; Kitazawa, H.; Kleybolte, L.; Klimenko, S.; Knee, A. M.; Knowles, T. D.; Knyazev, E.; Koch, P.; Koekoek, G.; Kojima, Y.; Kokeyama, K.; Koley, S.; Kolitsidou, P.; Kolstein, M.; Komori, K.; Kondrashov, V.; Kong, A. K. H.; Kontos, A.; Koper, N.; Korobko, M.; Kotake, K.; Kovalam, M.; Kozak, D. B.; Kozakai, C.; Kozu, R.; Kringel, V.; Krishnendu, N. V.; Kr'olak, A.; Kuehn, G.; Kuei, F.; Kuijer, P.; Kumar, A.; Kumar, P.; Kumar, Rahul; Kumar, Rakesh; Kume, J.; Kuns, K.; Kuo, C.; Kuo, H-S.; Kuromiya, Y.; Kuroyanagi, S.; Kusayanagi, K.; Kuwahara, S.; Kwak, K.; Lagabbe, P.; Laghi, D.; Lalande, E.; Lam, T. L.; Lamberts, A.; Landry, M.; Lane, B. B.; Lang, R. N.; Lange, J.; Lantz, B.; Rosa, I. La; Lartaux-Vollard, A.; Lasky, P. D.; Laxen, M.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lecoeuche, Y. K.; Lee, H. K.; Lee, H. M.; Lee, H. W.; Lee, J.; Lee, K.; Lee, R.; Lehmann, J.; Lemaitre, A.; Leonardi, M.; Leroy, N.; Letendre, N.; Levesque, C.; Levin, Y.; Leviton, J. N.; Leyde, K.; Li, A. K. Y.; Li, B.; Li, J.; Li, K. L.; Li, T. G. F.; Li, X.; Lin, C-Y.; Lin, F-K.; Lin, F-L.; Lin, H. L.; Lin, L. C.-C.; Linde, F.; Linker, S. D.; Linley, J. N.; Littenberg, T. B.; Liu, G. C.; Liu, J.; Liu, K.; Liu, X.; Llamas, F.; Llorens-Monteagudo, M.; Lo, R. K. L.; Lockwood, A.; London, L. T.; Longo, A.; Lopez, D.; Portilla, M. Lopez; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lott, T. P.; Lough, J. D.; Lousto, C. O.; Lovelace, G.; Lucaccioni, J. F.; Luck, H.; Lumaca, D.; Lundgren, A. P.; Luo, L.-W.; Lynam, J. E.; Macas, R.; MacInnis, M.; Macleod, D. M.; MacMillan, I. A. O.; Macquet, A.; Hernandez, I. Magana; Magazzu, C.; Magee, R. M.; Maggiore, R.; Magnozzi, M.; Mahesh, S.; Majorana, E.; Makarem, C.; Maksimovic, I.; Maliakal, S.; Malik, A.; Man, N.; Mandic, V.; Mangano, V.; Mango, J. L.; Mansell, G. L.; Manske, M.; Mantovani, M.; Mapelli, M.; Marchesoni, F.; Marchio, M.; Marion, F.; Mark, Z.; M'arka, S.; M'arka, Z.; Markakis, C.; Markosyan, A. S.; Markowitz, A.; Maros, E.; Marquina, A.; Marsat, S.; Martelli, F.; Martin, I. W.; Martin, R. M.; Martinez, M.; Martinez, V. A.; Martinez, V.; Martinovic, K.; Martynov, D. V.; Marx, E. J.; Masalehdan, H.; Mason, K.; Massera, E.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Mastrogiovanni, S.; Matas, A.; Mateu-Lucena, M.; Matichard, F.; Matiushechkina, M.; Mavalvala, N.; McCann, J. J.; McCarthy, R.; McClelland, D. E.; McClincy, P. K.; McCormick, S.; McCuller, L.; McGhee, G. I.; McGuire, S. C.; McIsaac, C.; McIver, J.; McRae, T.; McWilliams, S. T.; Meacher, D.; Mehmet, M.; Mehta, A. K.; Meijer, Q.; Melatos, A.; Melchor, D. A.; Mendell, G.; Menendez-Vazquez, A.; Menoni, C. S.; Mercer, R. A.; Mereni, L.; Merfeld, K.; Merilh, E. L.; Merritt, J. D.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Meyers, P. M.; Meylahn, F.; Mhaske, A.; Miani, A.; Miao, H.; Michaloliakos, I.; Michel, C.; Michimura, Y.; Middleton, H.; Milano, L.; Miller, A. L.; Miller, A.; Miller, B.; Millhouse, M.; Mills, J. C.; Milotti, E.; Minazzoli, O.; Minenkov, Y.; Mio, N.; Mir, Ll. M.; Miravet-Ten'es, M.; Mishra, C.; Mishra, T.; Mistry, T.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Miyakawa, O.; Miyamoto, A.; Miyazaki, Y.; Miyo, K.; Miyoki, S.; Mo, Geoffrey; Moguel, E.; Mogushi, K.; Mohapatra, S. R. P.; Mohite, S. R.; Molina, I.; Molina-Ruiz, M.; Mondin, M.; Montani, M.; Moore, C. J.; Moraru, D.; Morawski, F.; More, A.; Moreno, C.; Moreno, G.; Mori, Y.; Morisaki, S.; Moriwaki, Y.; Mours, B.; Mow-Lowry, C. M.; Mozzon, S.; Muciaccia, F.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, Soma; Mukherjee, Subroto; Mukherjee, Suvodip; Mukund, N.; Mullavey, A.; Munch, J.; Muniz, E. A.; Murray, P. G.; Musenich, R.; Muusse, S.; Nadji, S. L.; Nagano, K.; Nagano, S.; Nagar, A.; Nakamura, K.; Nakano, H.; Nakano, M.; Nakashima, R.; Nakayama, Y.; Napolano, V.; Nardecchia, I.; Narikawa, T.; Naticchioni, L.; Nayak, B.; Nayak, R. K.; Negishi, R.; Neil, B. F.; Neilson, J.; Nelemans, G.; Nelson, T. J. N.; Nery, M.; Neubauer, P.; Neunzert, A.; Ng, K. Y.; Ng, S. W. S.; Nguyen, C.; Nguyen, P.; Nguyen, T.; Quynh, L. Nguyen; Ni, W.-T.; Nichols, S. A.; Nishizawa, A.; Nissanke, S.; Nitoglia, E.; Nocera, F.; Norman, M.; North, C.; Nozaki, S.; Nuttall, L. K.; Oberling, J.; O'Brien, B. D.; Obuchi, Y.; O'Dell, J.; Oelker, E.; Ogaki, W.; Oganesyan, G.; Oh, J. J.; Oh, K.; Oh, S. H.; Ohashi, M.; Ohishi, N.; Ohkawa, M.; Ohme, F.; Ohta, H.; Okada, M. A.; Okutani, Y.; Okutomi, K.; Olivetto, C.; Oohara, K.; Ooi, C.; Oram, R.; O'Reilly, B.; Ormiston, R. G.; Ormsby, N. D.; Ortega, L. F.; O'Shaughnessy, R.; O'Shea, E.; Oshino, S.; Ossokine, S.; Osthelder, C.; Otabe, S.; Ottaway, D. J.; Overmier, H.; Pace, A. E.; Pagano, G.; Page, M. A.; Pagliaroli, G.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pan, H.; Pan, K.; Panda, P. K.; Pang, H.; Pang, P. T. H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Panther, F. H.; Paoletti, F.; Paoli, A.; Paolone, A.; Parisi, A.; Park, H.; Park, J.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patel, M.; Pathak, M.; Patricelli, B.; Patron, A. S.; Patrone, S.; Paul, S.; Payne, E.; Pedraza, M.; Pegoraro, M.; Pele, A.; Arellano, F. E. Pena; Penn, S.; Perego, A.; Pereira, A.; Pereira, T.; Perez, C. J.; P'erigois, C.; Perkins, C. C.; Perreca, A.; Perries, S.; Petermann, J.; Petterson, D.; Pfeiffer, H. P.; Pham, K. A.; Phukon, K. S.; Piccinni, O. J.; Pichot, M.; Piendibene, M.; Piergiovanni, F.; Pierini, L.; Pierro, V.; Pillant, G.; Pillas, M.; Pilo, F.; Pinard, L.; Pinto, I. M.; Pinto, M.; Piotrzkowski, K.; Pirello, M.; Pitkin, M. D.; Placidi, E.; Planas, L.; Plastino, W.; Pluchar, C.; Poggiani, R.; Polini, E.; Pong, D. Y. T.; Ponrathnam, S.; Popolizio, P.; Porter, E. K.; Poulton, R.; Powell, J.; Pracchia, M.; Pradier, T.; Prajapati, A. K.; Prasai, K.; Prasanna, R.; Pratten, G.; Principe, M.; Prodi, G. A.; Prokhorov, L.; Prosposito, P.; Prudenzi, L.; Puecher, A.; Punturo, M.; Puosi, F.; Puppo, P.; Purrer, M.; Qi, H.; Quetschke, V.; Quitzow-James, R.; Raab, F. J.; Raaijmakers, G.; Radkins, H.; Radulesco, N.; Raffai, P.; Rail, S. 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title: Constraints on dark photon dark matter using data from LIGO's and Virgo's third observing run
date: 2021-05-27
journal: nan
DOI: 10.1103/physrevd.105.063030
sha: 62e4d40961d564151010dcea7dd11c33ca9c5bdd
doc_id: 306431
cord_uid: 4vfxf4v5

We present a search for dark photon dark matter that could couple to gravitational-wave interferometers using data from Advanced LIGO and Virgo's third observing run. To perform this analysis, we use two methods, one based on cross-correlation of the strain channels in the two nearly aligned LIGO detectors, and one that looks for excess power in the strain channels of the LIGO and Virgo detectors. The excess power method optimizes the Fourier Transform coherence time as a function of frequency, to account for the expected signal width due to Doppler modulations. We do not find any evidence of dark photon dark matter with a mass between $m_{rm A} sim 10^{-14}-10^{-11}$ eV/$c^2$, which corresponds to frequencies between 10-2000 Hz, and therefore provide upper limits on the square of the minimum coupling of dark photons to baryons, i.e. $U(1)_{rm B}$ dark matter. For the cross-correlation method, the best median constraint on the squared coupling is $sim1.31times10^{-47}$ at $m_{rm A}sim4.2times10^{-13}$ eV/$c^2$; for the other analysis, the best constraint is $sim 2.4times 10^{-47}$ at $m_{rm A}sim 5.7times 10^{-13}$ eV/$c^2$. These limits improve upon those obtained in direct dark matter detection experiments by a factor of $sim100$ for $m_{rm A}sim [2-4]times 10^{-13}$ eV/$c^2$, and are, in absolute terms, the most stringent constraint so far in a large mass range $m_Asim$ $2times 10^{-13}-8times 10^{-12}$ eV/$c^2$.

Dark matter has been known to exist for decades [1] , yet its physical nature has remained elusive. Depending on the theory, dark matter could consist of particles with masses as low as 10 −22 eV/c 2 [2] , or as high as (sub-) solar-mass primordial black holes [3] [4] [5] [6] . Furthermore, dark matter clouds could form around black holes that deplete over time and emit gravitational waves [7, 8] .

Here, we focus on a subset of the "ultralight" dark matter regime, i.e. masses of O(10 −14 − 10 −11 ) eV/c 2 [9] , in which a variety of dark matter candidates may interact with gravitational-wave interferometers. Scalar, dilaton dark matter could change the mass of the electron and other physical constants, causing oscillations in the Bohr radius of atoms in various components of the interferometer [10] ; axions [11] could alter the phase velocities of circularly polarized photons in the laser beams traveling down each arm of the detector [12] ; dark photons could couple to baryons in the mirrors, causing an oscillatory force on the detector [13] ; tensor bosons could also interact with the interferometer in an analogous way as gravitational waves [14] . Here, we focus on dark photon dark matter whose relic abundance could be induced by the misalignment mechanism [15] [16] [17] , the tachyonic instability of a scalar field [18] [19] [20] [21] , or cosmic string network decays [22] . Cosmic strings, in particular, also offer a promising way to probe physics beyond the standard model with gravitational-wave detectors at energies much larger than those attainable by particle accelerators [23] , which complements the kind of direct dark matter search we perform here. Independently of the formation mechanism, analyses of gravitational-wave data could make a statement on the existence of dark photons. * Full author list given at the end of the article.

A search for dark photons using data from Advanced LIGO/Virgo's first observing run [13, 24] has already been performed, resulting in competitive constraints on the coupling of dark photons to baryons. Furthermore, scalar, dilaton dark matter was searched for recently using data from GEO600 [25] , and upper limits were placed on the degree to which scalar dark matter could have altered the electron mass or fine-structure constant [26] .

Other experiments that have probed the ultralight dark matter regime include the Eöt-Wash experiment, which aims to find a violation to the equivalence principle of General Relativity resulting from a new force acting on test masses in a dark matter field, by looking for a difference in the horizontal accelerations of two different materials using a continuously rotating torsion balance [27, 28] ; the MICROSCOPE satellite [29] , which measures the accelerations of two freely-floating objects in space made of different materials to look for a violation of the equivalence principle and hence a new force [30] ; the Axion Dark Matter Experiment (ADMX), which searches for O(µeV/c 2 ) dark matter by trying to induce an axion-to-photon conversion in the presence of a strong magnetic field in a resonant cavity [31] ; and the Any Light Particle Search (ALPS), which looks for particles with masses less than O(meV/c 2 ) (that could compose dark matter) by subjecting photons to strong magnetic fields in two cavities, separated by an opaque barrier, to cause a transition to an axion and then back to a photon [32] . Ultralight dark matter has also been constrained by observing gravitational waves from depleting boson clouds around black holes [8, [33] [34] [35] [36] [37] [38] , and by analyzing binary mergers, e.g. GW190521, which is consistent with the merger of complex vector boson stars [39] .

Compared to the analysis on data from LIGO/Virgo's first observing run [24] , we use two methods, one based on cross-correlation [13] , and another that judiciously varies the Fourier Transform coherence time [40, 41] , to search for dark photons in Advanced LIGO and Virgo arXiv:2105.13085v2 [astro-ph.CO] 2 Apr 2022 data from the third observing run (O3). Additionally, we include the signal induced by the common motion of the mirrors [42] -see section II. Although we do not find any evidence for a dark photon signal, we place stringent upper limits on the degree to which dark photons could have coupled to the baryons in the interferometer.

Ultralight dark photon dark matter is expected to cause time-dependent oscillations in the mirrors of the LIGO/Virgo interferometers, which would lead to a differential strain on the detector. We formulate dark photons in an analogous way to ordinary photons: as having a vector potential with an associated dark electric field that causes a quasi-sinusoidal force on the mirrors in the interferometers. The Lagrangian L that characterizes the dark photon coupling to a number current density J µ of baryons or baryons minus leptons is:

where F µν = ∂ µ A ν − ∂ ν A µ is the electromagnetic field tensor, is the reduced Planck's constant, c is the speed of light, µ 0 is the magnetic permeability in vacuum, m A is the dark photon mass, A µ is the four-vector potential of the dark photon, e is the electric charge, and is the strength of the particle/dark photon coupling normalized by the electromagnetic coupling constant. 1 If the analysis observation time exceeds the signal coherence time, given by equation 3 [41] , we can write the acceleration of the identical LIGO/Virgo mirrors in the dark photon field as [24] :

where ω, k, and A are the angular frequency, propagation vector, and polarization vector of the dark photon field, x is the position of a mirror, φ is a random phase, and q and M are the charge and the mass of the mirror, respectively. If the dark photon couples to the baryon number, q is the number of protons and neutrons in each mirror.

If it couples to the difference between the baryon and lepton numbers, q is the number of neutrons in each mirror. For a fused Silica mirror, q/M = 5.61 × 10 26 charges/kg for baryon coupling and q/M = 2.80 × 10 26 charges/kg for baryon-lepton coupling. Practically, we cannot distinguish between the two types of coupling, though the baryon-lepton coupling would lead to half the acceleration relative to that of the baryon coupling.

Because we observe for almost one year, significantly longer than the assumed dark photon coherence time, and the dark photons travel with non-relativistic velocities, we model the signal as a superposition of many plane waves, each with a velocity drawn from a Maxwell-Boltzmann distribution [43] . The superposition of dark photon plane waves with different velocities leads to a frequency variation of the signal [13, 41] :

where v 0 220 km/s is the velocity at which dark matter orbits the center of our galaxy, i.e. the virial velocity [44] , and the frequency f 0 is:

Dark photons cause small motions of an interferometer's mirrors, and lead to an observable effect in two ways. Firstly, the mirrors are well-separated from each other and hence experience slightly different dark photon dark matter phases. Such a phase difference leads to a differential change of the arm length, suppressed by v 0 /c. A simple relation between dark photon parameters and the effective strain h D can be written as [13] :

where 0 is the permittivity of free space, and C = √ 2/3 is a geometrical factor obtained by averaging over all possible dark photon propagation and polarization directions. Equation 5 can be derived by integrating equation 2 twice over time, dividing by the arm length of the interferometer, and performing the averages over time and the dark photon polarization and propagation directions.

Secondly, the common motion of the interferometer mirrors, induced by the dark photon dark matter background, can lead to an observable signal because of the finite travel time of the laser light in the interferometer arms. The light will hit the mirrors at different times during their common motions, and although the common motions do not change the instantaneous arm length, they can lead to a longer round-trip travel time for the light, equivalent to arm lengthening, and therefore an apparent differential strain [42] . Instead of being suppressed by v 0 /c as shown in equation 5, such an effect suffers from a suppression factor of (f 0 L/c), where L is the arm length of the interferometers. Similarly to equation 5, the common motion induces an observable signal with an effective strain h C as:

h D maps to h 2 in [42] , and h C is the result of a Taylor expansion of h 1 in [42] . The interference between the two contributions to the strain averages to zero over time, which indicates that the total effective strain can be written as

Cross-correlation has been widely used in gravitational-wave searches [45] [46] [47] , but is employed differently here. Because we are interested in ultralight dark matter, the coherence length of a dark photon signal, given by equation 2 in [41] , is always much larger than the separation between earth-based detectors [19] . Therefore, the interferometers should experience almost the same dark photon dark matter field, and the signals at any two detectors are highly correlated [19] .

Because the dark photon signal is quasimonochromatic, we analyze the frequency domain by Discrete Fourier Transforming the strain time series. Given a total coincident observation time, T obs , for two detectors, we divide the time series into N FFT smaller segments, with durations T FFT , i.e. T obs = N FFT T FFT . For the i-th time segment, j-th frequency bin, and interferometer k (1 or 2), we label the complex Discrete Fourier Transform coefficients as z k,ij . The one-sided power spectral densities (PSDs) of interferometer 1 (2) can be estimated by taking a (bias-corrected) running median of the raw noise powers P k,ij from 50 neighboring frequency bins: PSD k,ij = 2P k,ij /T FFT .

The cross-correlated signal strength is:

where " * " is the complex conjugate, and the variance is:

where ... NFFT is the average over N FFT time segments. Therefore, the signal-to-noise ratio (SNR) is:

In Gaussian noise without a signal, SN R j has zero mean and unit variance. The presence of a signal would lead to a non-zero offset in the mean SNR proportional to 2 (see equations [5] [6] . We note that we will include the overlap reduction function (ORF) in our upper limit calculation, which accounts for the relative orientation and overlap of two detectors and the responses of the detectors to a signal. As indicated in [13] , the ORF is constant (∼ −0.9) for the LIGO Hanford (H1) and LIGO Livingston (L1) detectors because the dark photon coherence length always exceeds the detector separation.

Here, we analyze only time segments satisfying standard data quality requirements used in gravitationalwave searches (see section IV), and further restrict to contiguous, coincident intervals of good data spanning the Fast Fourier Transform coherence time. As in the analysis performed using data from the first observing run (O1) [24] , we set T FFT = 1800 s, a pragmatic compromise between recovering signal power at high frequencies with shorter-than-optimal coherence times, and reducing noise contamination at low frequencies for longer-thanoptimal coherence times. An important constraint at low frequencies is that requiring longer (contiguous) coherence times necessarily reduces total available livetime, especially given the need for coincident H1 and L1 data. In total, we analyze 7539 pairs of 1800-second coincident time segments from H1 and L1.

In addition to cross-correlation, we employ an independent method [41] to search for dark photon dark matter. The method relies on Band Sampled Data (BSD) structures, which store the detector's downsampled strain data as a reduced analytic signal [40] in 10-Hz/1-month chunks. In each 10-Hz band, we change the Fast Fourier Transform coherence time [40] based on the expected Maxwell-Boltzmann frequency spread of dark photons, equation 3. Although this frequency spread is given as a function of v 0 , we instead use the escape velocity from the galaxy, v esc 540 km/s [44] , to determine the maximum allowed T FFT , T FFT,max , by requiring that the frequency spread be contained in one frequency bin in T FFT,max :

Based on simulations [41] , we found that the sensitivity of the search improves when taking T FFT = 1.5T FFT,max , because the power lost due to over-resolving in frequency is less than that gained by increasing T FFT . After selecting T FFT , we create time/frequency "peakmaps" [48, 49] , which are collections of ones and zeros that represent when the power in particular frequency bins has exceeded a threshold in the equalized spectrum. Because we choose T FFT to confine a signal's power to one frequency bin, we project the peakmap onto the frequency axis and look for frequency bins with large numbers of peaks, which we call the "number count".

FIG. 1. Number of candidates selected as a function of frequency in the BSD analysis, with log 10 TFFT colored. We select enough candidates in each 1-Hz band such that one coincident candidate between two detectors would occur in Gaussian noise. The changing number of candidates as a function of frequency ensures that we select uniformly in frequency.

We should uniformly select candidates in the frequency domain. In figure 1 shows how many candidates to select in each 10-Hz frequency band such that we would obtain, on average, one coincident candidate every one Hz in Gaussian noise. We also show in color how log 10 T FFT changes with frequency (equation 10).

Our detection statistic is the critical ratio CR:

where y is the number count in a particular frequency bin, and µ and σ are the mean and standard deviations of the number counts across all frequency bins in the band. The CR has a normal distribution with an expectation value of 0 and unit variance in Gaussian noise, and a normalized non-central χ 2 distribution with two degrees of freedom when a signal is present.

We use data from the third observing run (O3) of the Advanced LIGO [50] and Virgo [51] gravitational-wave detectors between 10-2000 Hz. O3 lasted from 2019 April 1 to 2020 March 27, with a one-month pause in data collection in October 2019. The three detectors' datasets, H1, L1, and Virgo (V1), had duty factors of ∼ 76%, ∼ 77%, and ∼ 76%, respectively, during O3.

In the event of a detection, calibration uncertainties would limit our ability to provide robust estimates of the coupling of dark matter to the interferometers. Even without a detection, these uncertainties affect the estimated instruments' sensitivities and inferred upper limits. The uncertainties vary over the course of a run but do not change by large values, so we do not consider time-dependent calibration uncertainties here [52] .

For the LIGO O3 data set, the analyses use the "C01" calibration, which has estimated maximum amplitude and phase uncertainties of ∼ 7% and ∼ 4 deg, respectively [52] . Because of the presence of a large number of noise artifacts, gating [53, 54] has also been applied to LIGO data. This procedure applies an inverse Tukey window to LIGO data at times when the root-meansquare value of the whitened strain channel in the 25-50 Hz band or 70-110 Hz band exceeds a certain threshold. The improvements from gating are significant, as seen in stochastic and continuous gravitational-wave analyses in O3 [46] . For the Virgo O3 dataset, we use the "V0" calibration with estimated maximum amplitude and phase uncertainties of 5% and 2 deg, respectively.

The output of the cross-correlation analysis is a value of the SNR in every frequency bin analyzed. At this point, we remove frequency bins with noise artifacts, i.e. bins within 0.056 Hz of known noise lines [55] . To further estimate the non-Gaussian background from artifacts, control samples are constructed using frequency lags, i.e. examining the correlations among a set of offset bins. We apply ten lags of the frequency bin offsets, i.e. (-50, -40, . .., -10, +10, ..., +50). If any frequency bin in the control sample has a |Re(SNR)| or |Im(SNR)| larger than 4.0 within 0.1 Hz of the outlier, the outlier is vetoed as contaminated by spectral leakage from a nearby non-Gaussian artifact. We choose a band of 0.1 Hz because within that band, spectral leakage causes non-physical correlated amplitudes and phases. Furthermore, ten lags allows us to compare frequency bins that are not too far from each other to construct an estimation of the noise in the chosen frequency bin.

After removing these instrumental artifacts, we look for frequency bins with Re(SNR) < −5.8, which corresponds to an overall ∼ 1% false alarm probability after including the trial factor in Gaussian noise, and is negative because H1 and L1 are rotated 90 deg with respect to each other. We find no outliers that pass this threshold.

Finally, as a cross-check, between [5.0, 5.8] for |Re(SNR)| or |Im(SNR)|, we find four non-vetoed outliers, which are shown in table I. The number of outliers is consistent with the Gaussian noise expectation of 4.1. We consider the absolute value of the real and imaginary components of the SNR because we are checking consistency with the expected number of outliers in Gaussian noise, which does not depend on the sign of the SNR . We show the distribution of the real and imaginary parts of the SNR in appendix A.

Before selecting candidates, we remove any frequencies that fall within one frequency bin of known noise lines from each detector's data [55] . We subsequently require coincident candidates between two or more detectors to be within one frequency bin of each other. At this stage, our analyses of the Hanford-Livingston (HL), Hanford-Virgo (HV), and the Livingston-Virgo (LV) baselines return 5801, 5628, and 5592 candidates, respectively.

In all baselines, we veto coincident candidates if one of the candidates' critical ratios is less than five or one of the candidates' frequencies is too close, i.e. within 5 bins, to the edges of the 10 Hz-band analyzed. The latter veto is necessary because the construction of the BSDs introduces artifacts in some bands at the edges. For the HL baseline, we remove candidates whose critical ratios differ by more than a factor of two because the sensitivity of each interferometer is comparable, so we do not expect a dark photon signal to appear with vastly different critical ratios in each detector. In the HV and LV baselines, we reject candidates whose critical ratios in V1 are higher than those in L1 or H1 because Virgo is less sensitive than LIGO [56] . We show distributions of CR in the Hanford and Livingston detectors across all frequencies in appendix A, figures 5 and 6, respectively, as well as the CR distribution of the number of coincident candidates in figure 7 .

We are then left with eleven surviving candidates across the three baselines, given in table II, that are all due to instrumental noise or artifacts in the peakmap. Peakmap artifacts occur because when there are strong lines at particular frequencies, we tend to select peaks that correspond to those lines. This causes a "depletion" of peaks nearby, and thus, a candidate could result because the level of the noise in the projected peakmap is lower on one side than on the other. No candidate has been found to be coincident in all three interferometers. These surviving candidates do not overlap with the list of known lines used in this search [55] , although line artifacts or/and combs regions are clearly visible when using a different resolution to construct the spectra. In figure 2, we show an example of the disturbances near an outlier at 1498.76 Hz, where a family of combs is present in both the H1 and L1 detectors.

Finding no evidence of a signal, in figure 3 we place 95% confidence-level upper limits on the square of the minimum detectable dark photon/baryon coupling, U (1) B , using the HL baseline. The cross-correlation limits are shown in red for every 0.556-mHz bin, while the BSD limits are given in black with cyan 1σ shading in frequency bins in which coincident candidates were found. To calculate these limits, we employ the Feldman-Cousins [57] approach, in which we assume that both FIG. 2. We discarded all surviving outliers because they were due to instrumental noise or artifacts. In this figure, we can see the comb affecting the power spectral density (PSD) of H1 and the line in L1 responsible for the production of an outlier near 1498.8 Hz. Frequency resolution: δf = 3.47 × 10 −5 Hz.

CR and SN R follow Gaussian distributions, and map the measured detection statistics to "inferred" positivedefinite statistics based on the upper value of table 10 of [57] at 95% confidence. As shown in [5] , this approach produces consistent limits with respect to those that would be obtained by injecting simulated signals. With our estimates of the noise power spectral density and T FFT , we can translate the inferred SN R and CR at each frequency to the corresponding signal amplitude using equation 9 in [13] and equation 30 in [41] , respectively. This amplitude is then converted to a coupling strength using equation 5, and adjusted for the common mode motion effect [42] .

The limits from the cross-correlation analysis are more stringent than those from the BSD method because the former employs the phase information of the signal, while the latter only looks at power. Furthermore, though the choice of T FFT is "optimal" in the BSD method, it is still shorter than that used by cross correlation by as much as a factor of six above ∼ 330 Hz, and the definition of "optimal" depends on whether we consider the escape or virial velocity of dark matter as responsible for the frequency variation. Additionally, cross-correlation of two data streams can achieve better sensitivity than coincidence analysis of the same streams (for the same livetime) because coincidence analysis is limited by the less sensitive of the two detectors at a given frequency. TABLE I. Four sub-threshold outliers returned by the cross correlation analysis of the HL baseline. We report the (complex) signal-to-noise ratio (SNR) for each outlier and the associated background (Bkg) SNR. For the background SNR, we include the range of the real part (Re) and imaginary part (Im) among ten lagged results. These four events are consistent with the Gaussian noise expectation over all of the clean bands in the analysis. The limits from each method are comparable, noting that the BSD-based analysis takes an optimally chosen TFFT and can observe for twice as long than the cross-correlation method can. We plot for comparison upper limits from MICROSCOPE given in [30] , though other weaker limits exist [59] [60] [61] , that have been converted from the coupling constant to gravity, α, to 2 , using the equation below figure 3 in [62] , and from the Eöt-Wash torsion balance experiment [28] . To produce limits on dark photon/baryon-lepton coupling, U (1)B−L, our limits should be multiplied by four.

We have presented strong constraints on the coupling strength of dark photon dark matter to baryons by using data from LIGO's and Virgo's third observing run. In the mass range m A ∼ [2 − 4] × 10 −13 eV/c 2 , we improve upon previous limits derived using data from the first observing run of LIGO [24] by a factor of ∼ 100 in the square of the coupling strength of dark photons to baryons. This improvement is due to more sensitive detectors and to accounting for the finite light travel time [42] . Additionally, our limits surpass those of existing dark matter experiments, such as the Eöt-Wash torsion balance and MICROSCOPE, by orders of magnitude in certain frequency bands, and support new ways to use gravitational-wave detectors as direct probes of the existence of ultralight dark matter. As the sensitivities of current ground-based gravitational-wave detectors improve, and third generation detectors, such as Cosmic Explorer [63] and Einstein Telescope [64] , come online, we will dig even more deeply into the noise. Furthermore, once future-generation space-based detectors, such as DECIGO [65] , LISA [66] , and TianQin [67] , are operational, we will probe dark photon couplings at masses as low as m A ∼ 10 −18 eV/c 2 .

This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors also gratefully acknowledge the support of the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. The authors gratefully acknowledge the Italian Istituto Nazionale di Fisica Nucleare (INFN), the French Centre National de la Recherche Scientifique (CNRS) and the Netherlands Organization for Scientific Research, for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by We would like to thank all of the essential workers who put their health at risk during the COVID-19 pandemic, without whom we would not have been able to complete this work.

We provide here more details on our detection statistics for both methods. When we calculate upper limits, we assume that these statistics follow Gaussian distributions, which is actually true only in clean bands. But, because we showed the Feldman-Cousins approach to be robust towards noise disturbances in [41] , we are confident that the limits are reflective of what we would have obtained if we performed software injections.

For the cross-correlation search, the distributions of the real and imaginary parts of the SNR are shown in figure 4 after vetoing frequency bins within 0.056Hz of the known noise lines [55] and after vetoing the instrumental artifacts as described in the main text above.

We show the distributions of the CR in Hanford (figure 5) and Livingston (figure 6), over all frequency bins between 10-2000 Hz. We also overlay a Gaussian on the plot to show the extent to which the distributions differs from a Gaussian distribution. In both detectors, the number of frequency bins whose CRs deviate from Gaussianity is of O(10 2 ), which is a small fraction of the total number of bins analyzed.

We also include a plot to characterize the coincident candidates between Hanford and Livingston that are selected in our search. Figure 7 shows a histogram of all the coincident candidates' critical ratios that we select, as well as a black line that indicates the threshold on the critical ratio that we impose, equal to 5. We can see that very few candidates are coincident relative to the number of candidates plotted in figures 5 and 6, and that the total number of coincident candidates that are subject to further study is of O(1000). Histogram of coincident critical ratios, after our selection of candidates in each 10-Hz frequency band. We performed the coincidences between the candidates returned after analyzing Hanford and Livingston data.

Virgo, KAGRA)

Ballmer, 58 A. Balsamo, 54 G. Baltus, 59 S. Banagiri, 60 D. Bankar, 11

66 A. C. Baylor, 7 M. Bazzan, 74, 75 B. Bécsy, 76 V. M. Bedakihale, 77 M. Bejger, 78 I. Belahcene

S. Biscans, 1, 67 M. Bischi, 46, 47 S. Biscoveanu, 67 A. Bisht, 9, 10 B. Biswas, 11 M. Bitossi, 40

66 A. Brillet, 92 M. Brinkmann, 9

70 L. Cadonati, 104 G. Cagnoli, 24 C. Cahillane, 64

111 M. Cannavacciuolo, 93 K. C. Cannon, 112 H. Cao, 80 Z. Cao, 113 E. Capocasa, 20 E. Capote

86 F. Cavalier, 39 R. Cavalieri, 40 M. Ceasar, 120 G. Cella, 18 P. Cerdá-Durán, 121 E. Cesarini

75 R. Chierici, 134 A. Chincarini, 82 M. L. Chiofalo

51 G. Ciani, 74, 75 P. Ciecielag, 78 M. Cieślar, 78 M. Cifaldi

143 A. Colombo, 61 M. Colpi

120 D. DeBra, 70 M. Deenadayalan, 11 J. Degallaix, 155 M. De Laurentis, 23, 4 S. Deléglise, 99 V. Del Favero

59 M. Fazio, 163 J. Feicht, 1 M. M. Fejer, 70 E. Fenyvesi, 68, 164 D. L. Ferguson, 165 A. Fernandez-Galiana

Ferreira, 16 F. Fidecaro, 71, 18 P. Figura, 100 I. Fiori, 40 M. Fishbach, 15

13 D. Ganapathy, 67 A. Ganguly, 19 D. Gao, 174 S. G. Gaonkar, 11 B. Garaventa, 82, 110 C. García-Núñez, 90 C. García-Quirós

176 Shaon Ghosh, 7, 162 Shrobana Ghosh, 17 B. Giacomazzo, 61, 62, 63 L. Giacoppo

66 J. Healy, 123 A. Heidmann, 99 A. Heidt, 9

92 F. Hellman, 192 P. Hello, 39 A. F. Helmling-Cornell, 57 G. Hemming, 40 M. Hendry, 66 I. S. Heng

50 J. Hennig, 193 M. H. Hennig, 193 A. G. Hernandez, 81 F. Hernandez Vivanco, 5 M. Heurs

146 M. Kasprzack, 1 W. Kastaun, 9, 10 S. Katsanevas, 40 E. Katsavounidis, 67 W. Katzman, 6 T. Kaur

106 A. Lamberts, 92

67 A. Longo, 243, 244 D. Lopez, 158 M. Lopez Portilla, 111 M. Lorenzini, 117, 118 V. Loriette

110 S. Mahesh, 161 E. Majorana, 95, 48 C. Makarem, 1 I. Maksimovic, 245 S. Maliakal, 1 A. Malik, 84 N. Man, 92 V. Mandic, 60 V. Mangano, 95

40 M. Mapelli, 74, 75 F. Marchesoni, 247, 72, 248 M. Marchio, 20 F. Marion, 28 Z. Mark

43 Z. Márka, 43 C. Markakis, 12 A. S. Markosyan, 70 A. Markowitz, 1 E. Maros, 1 A. Marquina

189 B. Mours, 160 C. M. Mow-Lowry, 14, 171 S. Mozzon, 153 F. Muciaccia, 95, 48 Arunava Mukherjee

146 Soma Mukherjee, 148 Subroto Mukherjee, 77 Suvodip Mukherjee, 85 N

Panda, 205 H. Pang, 129 P. T. H. Pang, 50, 111 C. Pankow, 15 F. Pannarale, 95, 48 B. C. Pant

50 A. Pasqualetti, 40 R. Passaquieti, 71, 18 D. Passuello, 18 M. Patel, 54 M. Pathak, 80 B. Patricelli

92 M. Piendibene, 71, 18 F. Piergiovanni, 46, 47 L. Pierini, 95, 48 V. Pierro, 79, 94 G. Pillant, 40 M. Pillas

18 L. Pinard, 155 I. M. Pinto, 79, 94, 264 M. Pinto, 40 K. Piotrzkowski, 49 M. Pirello, 64 M. D. Pitkin

14 P. Prosposito, 117, 118 L. Prudenzi, 102 A. Puecher, 50, 111 M. Punturo, 72 F. Puosi, 18, 71 P. Puppo

183 G. Riemenschneider, 268, 22 K. Riles, 182 S. Rinaldi, 18, 71 K. Rink, 178 M

15 B. Shams, 169 L. Shao

109 D. Singh, 146 N. Singh, 100 A. Singha, 152, 50 A. M. Sintes, 142 V. Sipala, 115, 116 V. Skliris

280 R. Soulard, 92 T. Souradeep, 267, 11 E. Sowell, 145 V. Spagnuolo, 152, 50 A. P. Spencer, 66 M. Spera

Urban, 2 T. Ushiba, 190 A. Utina, 152, 50 H

Den Broeck, 111, 50 D. C. Vander-Hyde, 58 L. van der Schaaf, 50 J. V. van Heijningen, 49

295 N. van Remortel, 207 M. Vardaro, 240, 50 A. F. Vargas, 114 V. Varma, 177 M. Vasúth

14 G. Vedovato, 75 J. Veitch, 66 P. J. Veitch, 80 J. Venneberg, 9, 10 G. Venugopalan, 1 D. Verkindt

35 A. Zadrożny, 230 M. Zanolin, 33 S. Zeidler, 297 T. Zelenova, 40 J.-P. Zendri, 75 M. Zevin

Cambridge CB2 1TN, United Kingdom 13 Theoretisch-Physikalisches Institut

Dipartimento di Fisica e Astronomia, I-35131 Padova, Italy 75 INFN, Sezione di Padova, I-35131 Padova

USA 77 Institute for Plasma Research, Bhat, Gandhinagar 382428, India 78 Nicolaus Copernicus Astronomical Center

89 INFN, Trento Institute for Fundamental Physics and Applications, I-38123 Povo

France 93 Dipartimento di Fisica

Mumbai 400 076, India 98 INFN, Laboratori Nazionali del Gran Sasso, I-67100 Assergi

France 104 School of Physics

Università degli Studi di Genova, I-16146 Genova, Italy 111 Institute for Gravitational and Subatomic Physics (GRASP)

Australia 115 Università degli Studi di Sassari, I-07100 Sassari, Italy 116 INFN, Laboratori Nazionali del Sud, I-95125 Catania, Italy 117 Università di Roma Tor Vergata, I-00133 Roma, Italy 118 INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy 119 University of Sannio at Benevento, I-82100 Benevento, Italy and INFN, Sezione di Napoli, I-80100 Napoli

Ulsan National Institute of Science and Technology (UNIST)

Toyama 930-8555, Japan 190 Institute for Cosmic Ray Research (ICRR)

Niigata 950-2181, Japan 196 Department of Physics

Japan 205 Directorate of Construction, Services & Estate Management

Korea 210 National Astronomical Observatories, Chinese Academic of Sciences