Observation of Gravitational Waves from the Coalescence of a 2.5–4.5 M⊙ Compact Object and a Neutron Star
Creators
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Abac, A. G.1
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Abbott, R.2
- Abouelfettouh, I.3
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Acernese, F.4, 5
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Ackley, K.6
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Adhicary, S.7
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Adhikari, N.8
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Adhikari, R. X.2
- Adkins, V. K.9
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Agarwal, D.10, 11
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Agathos, M.12
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Aghaei Abchouyeh, M.13
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Aguiar, O. D.14
- Aguilar, I.15
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Aiello, L.16, 17, 18
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Ain, A.19
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Ajith, P.20
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Akçay, S.21
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Akutsu, T.22
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Albanesi, S.23, 24, 25
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Alfaidi, R. A.26
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Al-Jodah, A.27
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Alléné, C.28
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Allocca, A.5, 29
- Al-Shammari, S.18
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Altin, P. A.30
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Alvarez-Lopez, S.31
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Amato, A.32, 33
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Amez-Droz, L.34
- Amorosi, A.34
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Amra, C.35
- Ananyeva, A.2
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Anderson, S. B.2
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Anderson, W. G.2
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Andia, M.36
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Ando, M.37
- Andrade, T.38
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Andres, N.28
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Andrés-Carcasona, M.39
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Andrić, T.1, 40, 41
- Anglin, J.42
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Ansoldi, S.43, 44
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Antelis, J. M.45
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Antier, S.46
- Aoumi, M.37
- Appavuravther, E. Z.47, 48
- Appert, S.2
- Apple, S. K.49
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Arai, K.2
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Araya, A.37
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Araya, M. C.2
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Areeda, J. S.50
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Argianas, L.51
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Aritomi, N.3
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Armato, F.52, 53
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Arnaud, N.36, 54
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Arogeti, M.55
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Aronson, S. M.9
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Arun, K. G.56
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Ashton, G.57
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Aso, Y.22, 58
- Assiduo, M.59, 60
- Assis de Souza Melo, S.54
- Aston, S. M.3
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Astone, P.61
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Attadio, F.61, 62
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Aubin, F.63
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AultONeal, K.64
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Avallone, G.4
- Azrad, D.65
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Babak, S.66
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Badaracco, F.52
- Badger, C.67
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Bae, S.68
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Bagnasco, S.23
- Bagui, E.34
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Baier, J. G.69
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Baiotti, L.70
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Bajpai, R.22
- Baka, T.71
- Ball, M.72
- Ballardin, G.54
- Ballmer, S. W.73
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Banagiri, S.74
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Banerjee, B.41
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Bankar, D.11
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Baral, P.8
- Barayoga, J. C.2
- Barish, B. C.2
- Barker, D.3
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Barneo, P.38
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Barone, F.5, 4
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Barr, B.26
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Barsotti, L.31
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Barsuglia, M.66
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Barta, D.75
- Bartoletti, A. M.76
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Barton, M. A.26
- Bartos, I.42
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Basak, S.20
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Basalaev, A.77
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Bassiri, R.15
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Basti, A.78, 79
- Bates, D. E.18
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Bawaj, M.47, 80
- Baxi, P.81
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Bayley, J. C.26
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Baylor, A. C.8
- Baynard, P. A.55
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Bazzan, M.82, 83
- Bedakihale, V. M.84
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Beirnaert, F.85
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Bejger, M.86
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Belardinelli, D.17
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Bell, A. S.26
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Benedetto, V.87
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Benoit, W.88
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Bentara, I.89
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Bentley, J. D.77
- Ben Yaala, M.90
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Bera, S.91
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Berbel, M.92
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Bergamin, F.1, 40
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Berger, B. K.15
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Bernuzzi, S.24
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Beroiz, M.2
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Berry, C. P. L.26
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Bersanetti, D.52
- Bertolini, A.33
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Betzwieser, J.3
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Beveridge, D.27
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Bevins, N.51
- Bhandare, R.93
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Bhardwaj, U.33, 94
- Bhatt, R.2
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Bhattacharjee, D.69, 95
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Bhaumik, S.42
- Bhowmick, S.96
- Bianchi, A.33, 97
- Bilenko, I. A.98
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Billingsley, G.2
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Binetti, A.99
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Bini, S.100, 101
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Birnholtz, O.65
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Biscoveanu, S.74
- Bisht, A.40
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Bitossi, M.54, 79
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Bizouard, M.-A.46
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Blackburn, J. K.2
- Blagg, L. A.72
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Blair, C. D.27, 3
- Blair, D. G.27
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Bobba, F.4, 5
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Bode, N.1, 40
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Boileau, G.19, 46
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Boldrini, M.61, 62
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Bolingbroke, G. N.102
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Bolliand, A.35, 103
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Bonavena, L. D.82
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Bondarescu, R.38
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Bondu, F.104
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Bonilla, E.15
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Bonilla, M. S.50
- Bonino, A.105
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Bonnand, R.28
- Booker, P.1, 40
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Borchers, A.1, 40
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Boschi, V.79
- Bose, S.106
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Bossilkov, V.3
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Boudart, V.107
- Boudon, A.89
- Bozzi, A.54
- Bradaschia, C.79
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Brady, P. R.8
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Braglia, M.108
- Branch, A.3
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Branchesi, M.41, 109
- Brandt, J.55
- Braun, I.69
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Breschi, M.24
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Briant, T.110
- Brillet, A.46
- Brinkmann, M.1, 40
- Brockill, P.8
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Brockmueller, E.1, 40
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Brooks, A. F.2
- Brown, B. C.42
- Brown, D. D.102
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Brozzetti, M. L.47, 80
- Brunett, S.2
- Bruno, G.10
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Bruntz, R.111
- Bryant, J.105
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Bucci, F.60
- Buchanan, J.111
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Bulashenko, O.38
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Bulik, T.112
- Bulten, H. J.33
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Buonanno, A.1, 113
- Burtnyk, K.3
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Buscicchio, R.114, 115
- Buskulic, D.28
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Buy, C.116
- Byer, R. L.15
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Cabourn Davies, G. S.117
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Cabras, G.43, 44
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Cabrita, R.10
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Cáceres-Barbosa, V.7
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Cadonati, L.55
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Cagnoli, G.118
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Cahillane, C.73
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Calderón Bustillo, J.119
- Callister, T. A.120
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Calloni, E.5, 29
- Camp, J. B.121
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Canepa, M.52, 53
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Caneva Santoro, G.39
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Cannon, K. C.37
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Cao, H.102
- Capistran, L. A.122
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Capocasa, E.66
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Capote, E.73
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Carapella, G.4, 5
- Carbognani, F.54
- Carlassara, M.1, 40
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Carlin, J. B.123
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Carpinelli, M.54, 114, 124
- Carrillo, G.72
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Carter, J. J.1, 40
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Carullo, G.125
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Casanueva Diaz, J.54
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Casentini, C.16, 17, 126
- Castro-Lucas, S. Y.96
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Caudill, S.33, 71, 127
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Cavaglià, M.95
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Cavalieri, R.54
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Cella, G.79
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Cerdá-Durán, P.128
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Cesarini, E.17
- Chaibi, W.46
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Chakraborty, P.1, 40
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Chalathadka Subrahmanya, S.77
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Chan, J. C. L.125
- Chan, M.129
- Chandra, K.7
- Chang, R.-J.130
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Chao, S.131, 132
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Char, P.107
- Charlton, E. L.111
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Charlton, P.133
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Chassande-Mottin, E.66
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Chatterjee, C.134
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Chatterjee, Debarati11
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Chatterjee, Deep31
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Chattopadhyay, D.18
- Chaturvedi, M.93
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Chaty, S.66
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Chatziioannou, K.2
- Chen, A.12
- Chen, A. H.-Y.135
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Chen, D.22
- Chen, H.131
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Chen, H. Y.136
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Chen, J.31
- Chen, K. H.132
- Chen, Y.131
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Chen, Yanbei2
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Chen, Yitian137
- Cheng, H. P.138
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Chessa, P.47, 80
- Cheung, H. T.81
- Cheung, S. Y.139
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Chiadini, F.5, 4
- Chiarini, G.83
- Chierici, R.89
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Chincarini, A.52
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Chiofalo, M. L.78, 79
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Chiummo, A.5, 54
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Chou, C.135
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Choudhary, S.27
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Christensen, N.46
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Chua, S. S. Y.30
- Chugh, P.139
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Ciani, G.82, 83
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Ciecielag, P.86
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Cieślar, M.112
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Cifaldi, M.17
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Ciolfi, R.83, 140
- Clara, F.3
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Clark, J. A.2, 55
- Clarke, J.18
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Clarke, T. A.139
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Clearwater, P.141
- Clesse, S.34
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Coccia, E.39, 41, 109
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Codazzo, E.41
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Cohadon, P.-F.110
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Colace, S.53
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Colleoni, M.91
- Collette, C. G.34
- Collins, J.3
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Colloms, S.26
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Colombo, A.114, 115, 142
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Colpi, M.114, 115
- Compton, C. M.3
- Connolly, G.72
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Conti, L.83
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Corbitt, T. R.9
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Cordero-Carrión, I.128
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Corezzi, S.47, 80
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Cornish, N. J.143
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Corsi, A.144
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Cortese, S.54
- Costa, C. A.14
- Cottingham, R.3
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Coughlin, M. W.88
- Couineaux, A.61
- Coulon, J.-P.46
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Countryman, S. T.145
- Coupechoux, J.-F.89
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Couvares, P.2, 55
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Coward, D. M.27
- Cowart, M. J.3
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Coyne, R.146
- Craig, K.90
- Creed, R.18
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Creighton, J. D. E.8
- Creighton, T. D.147
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Cremonese, P.91
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Criswell, A. W.88
- Crockett-Gray, J. C. G.9
- Crook, S.3
- Crouch, R.3
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Csizmazia, J.3
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Cudell, J. R.107
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Cullen, T. J.2
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Cumming, A.26
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Cuoco, E.54, 79
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Cusinato, M.128
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Dabadie, P.118
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Dal Canton, T.36
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Dall'Osso, S.61
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Dal Pra, S.61
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Dálya, G.116
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D'Angelo, B.52
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Danilishin, S.32, 33
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D'Antonio, S.17
- Danzmann, K.1, 40
- Darroch, K. E.111
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Dartez, L. P.3
- Dasgupta, A.84
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Datta, S.56
- Dattilo, V.54
- Daumas, A.66
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Davari, N.124, 148
- Dave, I.93
- Davenport, A.96
- Davier, M.36
- Davies, T. F.27
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Davis, D.2
- Davis, L.27
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Davis, M. C.88
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Davis, P. J.149
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Dax, M.1
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De Bolle, J.85
- Deenadayalan, M.11
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Degallaix, J.89
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De Laurentis, M.5, 29
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Deléglise, S.110
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De Lillo, F.10
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Dell'Aquila, D.124, 148
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Del Pozzo, W.78, 79
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De Marco, F.61, 62
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De Matteis, F.16, 17
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D'Emilio, V.2
- Demos, N.31
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Dent, T.119
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Depasse, A.10
- DePergola, N.51
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De Pietri, R.150, 151
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De Rosa, R.5, 29
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De Rossi, C.54
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DeSalvo, R.87, 5
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De Simone, R.4
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Dhani, A.1
- Diab, R.42
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Díaz, M. C.147
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Di Cesare, M.29
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Dideron, G.152
- Didio, N. A.73
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Dietrich, T.1
- Di Fiore, L.5
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Di Fronzo, C.34
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Di Giovanni, M.61, 62
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Di Girolamo, T.5, 29
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Diksha, D.32, 33
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Di Michele, A.80
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Ding, J.66, 153
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Di Pace, S.61, 62
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Di Palma, I.61, 62
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Di Renzo, F.89
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Divyajyoti154
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Dmitriev, A.105
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Doctor, Z.74
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Dohmen, E.3
- Doleva, P. P.111
- Dominguez, D.155
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D'Onofrio, L.61
- Donovan, F.31
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Dooley, K. L.18
- Dooney, T.71
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Doravari, S.11
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Dorosh, O.156
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Drago, M.61, 62
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Driggers, J. C.3
- Ducoin, J.-G.66, 157, 158
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Dunn, L.123
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Dupletsa, U.41
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D'Urso, D.124, 148
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Duval, H.159
- Duverne, P.-A.36
- Dwyer, S. E.3
- Eassa, C.3
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Ebersold, M.28
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Eckhardt, T.77
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Eddolls, G.73
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Edelman, B.72
- Edo, T. B.2
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Edy, O.117
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Effler, A.3
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Eichholz, J.30
- Einsle, H.46
- Eisenmann, M.22
- Eisenstein, R. A.31
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Ejlli, A.18
- Eleveld, R. M.160
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Emma, M.57
- Endo, K.161
- Engl, A. J.15
- Enloe, E.55
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Errico, L.5, 29
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Essick, R. C.162
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Estellés, H.1
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Estevez, D.63
- Etzel, T.2
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Evans, M.31
- Evstafyeva, T.163
- Ewing, B. E.7
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Ezquiaga, J. M.125
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Fabrizi, F.59, 60
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Faedi, F.59, 60
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Fafone, V.16, 17
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Fairhurst, S.18
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Farah, A. M.120
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Farr, B.72
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Farr, W. M.164, 165
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Favaro, G.82
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Favata, M.166
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Fays, M.107
- Fazio, M.90
- Feicht, J.2
- Fejer, M. M.15
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Felicetti, R. .167
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Fenyvesi, E.75, 168
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Ferguson, D. L.136
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Ferraiuolo, S.61, 62, 169
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Ferrante, I.78, 79
- Ferreira, T. A.9
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Fidecaro, F.78, 79
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Figura, P.86
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Fiori, A.78, 79
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Fiori, I.54
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Fishbach, M.162
- Fisher, R. P.111
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Fittipaldi, R.5, 170
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Fiumara, V.5, 171
- Flaminio, R.28
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Fleischer, S. M.172
- Fleming, L. S.173
- Floden, E.88
- Foley, E. M.88
- Fong, H.129
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Font, J. A.128
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Fornal, B.174
- Forsyth, P. W. F.30
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Franceschetti, K.150
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Franchini, N.66
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Frasca, S.61, 62
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Frasconi, F.79
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Frattale Mascioli, A.61, 62
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Frei, Z.175
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Freise, A.33, 97
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Freitas, O.128, 176
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Frey, R.72
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Frischhertz, W.3
- Fritschel, P.31
- Frolov, V. V.3
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Fronzé, G. G.23
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Fuentes-Garcia, M.2
- Fujii, S.37
- Fujimori, T.177
- Fulda, P.42
- Fyffe, M.3
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Gadre, B.71
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Gair, J. R.1
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Galaudage, S.46
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Galdi, V.87, 5
- Gallagher, H.178
- Gallardo, S.179
- Gallego, B.179
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Gamba, R.24
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Gamboa, A.1
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Ganapathy, D.31
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Ganguly, A.11
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Garaventa, B.52, 53
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García-Bellido, J.108
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García Núñez, C.173
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García-Quirós, C.180
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Gardner, J. W.30
- Gardner, K. A.129
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Gargiulo, J.54
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Garron, A.91
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Garufi, F.5, 29
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Gasbarra, C.16, 17
- Gateley, B.3
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Gayathri, V.8
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Gemme, G.52
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Gennai, A.79
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Gennari, V.116
- George, J.93
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George, R.136
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Gerberding, O.77
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Gergely, L.181
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Ghonge, S.55
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Ghosh, Archisman85
- Ghosh, Sayantan182
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Ghosh, Shaon166
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Ghosh, Shrobana1, 40
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Ghosh, Suprovo11
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Ghosh, Tathagata11
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Giacoppo, L.61, 62
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Giaime, J. A.9, 3
- Giardina, K. D.3
- Gibson, D. R.173
- Gibson, D. T.163
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Gier, C.90
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Giri, P.78, 79
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Gissi, F.87
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Gkaitatzis, S.78, 79
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Glanzer, J.9
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Glotin, F.36
- Godfrey, J.72
- Godwin, P.2
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Goebbels, N. L.77
-
Goetz, E.129
- Golomb, J.2
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Gomez Lopez, S.61, 62
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Goncharov, B.41
- Gong, Y.183
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González, G.9
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Goodarzi, P.184
- Goode, S.139
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Goodwin-Jones, A. W.27
- Gosselin, M.54
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Göttel, A. S.18
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Gouaty, R.28
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Gould, D. W.30
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Govorkova, K.31
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Goyal, S.1
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Grace, B.30
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Grado, A.5, 185
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Graham, V.26
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Granados, A. E.88
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Granata, M.89
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Granata, V.4
- Gras, S.31
- Grassia, P.2
- Gray, A.88
- Gray, C.3
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Gray, R.26
- Greco, G.47
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Green, A. C.33, 97
- Green, S. M.117
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Green, S. R.186
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Gretarsson, A. M.64
- Gretarsson, E. M.64
- Griffith, D.2
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Griffiths, W. L.18
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Griggs, H. L.55
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Grignani, G.47, 80
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Grimaldi, A.100, 101
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Grimaud, C.28
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Grote, H.18
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Guerra, D.128
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Guetta, D.61, 187
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Guidi, G. M.59, 60
- Guimaraes, A. R.9
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Gulati, H. K.84
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Gulminelli, F.149
- Gunny, A. M.31
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Guo, H.174
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Guo, W.27
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Guo, Y.32, 33
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Gupta, Anchal2
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Gupta, Anuradha188
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Gupta, Ish7
- Gupta, N. C.84
- Gupta, P.33, 71
- Gupta, S. K.42
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Gupta, T.143
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Gupte, N.1
- Gurs, J.77
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Gutierrez, N.89
-
Guzman, F.122
- H, H.-Y.131
- Haba, D.155
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Haberland, M.1
- Haino, S.189
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Hall, E. D.31
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Hamilton, E. Z.91
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Hammond, G.26
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Han, W.-B.190
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Haney, M.33
- Hanks, J.3
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Hanna, C.7
- Hannam, M. D.18
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Hannuksela, O. A.191
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Hanselman, A. G.120
- Hansen, H.3
- Hanson, J.3
- Harada, R.37
- Hardison, A. R.192
- Haris, K.33, 71
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Harmark, T.125
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Harms, J.41, 109
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Harry, G. M.193
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Harry, I. W.117
- Hart, J.69
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Haskell, B.86
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Haster, C.-J.194
- Hathaway, J. S.178
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Haughian, K.26
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Hayakawa, H.37
- Hayama, K.195
- Hayes, R.18
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Heffernan, A.91
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Heidmann, A.110
- Heintze, M. C.3
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Heinze, J.105
- Heinzel, J.31
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Heitmann, H.46
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Hellman, F.196
- Hello, P.36
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Helmling-Cornell, A. F.72
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Hemming, G.54
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Henderson-Sapir, O.102
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Hendry, M.26
- Heng, I. S.26
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Hennes, E.33
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Henshaw, C.55
- Hertog, T.99
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Heurs, M.1, 40
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Hewitt, A. L.163, 197
- Heyns, J.31
- Higginbotham, S.18
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Hild, S.32, 33
- Hill, S.26
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Himemoto, Y.198
- Hirata, N.22
- Hirose, C.199
- Hoang, S.36
- Hochheim, S.1, 40
- Hofman, D.89
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Holland, N. A.33, 97
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Holley-Bockelmann, K.134
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Holmes, Z. J.102
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Holz, D. E.120
- Honet, L.34
- Hong, C.15
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Zweizig, J.2
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205.
Aoyama Gakuin University
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207.
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Jagiellonian University
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Chung-Ang University
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214.
Aix-Marseille University
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Institut des Hautes Études Scientifiques
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Laboratory Universe and Theories
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PSL Research University
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256.
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Abstract
We report the observation of a coalescing compact binary with component masses 2.5–4.5 M⊙ and 1.2–2.0 M⊙ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO–Virgo–KAGRA detector network on 2023 May 29 by the LIGO Livingston observatory. The primary component of the source has a mass less than 5 M⊙ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of 55^(+127)_(-47) Gpc⁻³ yr ⁻¹ for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star–black hole merger, GW230529_181500-like sources may make up the majority of neutron star–black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star–black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap.
Copyright and License
© 2024. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Acknowledgement
This material is based on 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 GEO 600 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 (NWO) 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 the Council of Scientific and Industrial Research of India, the Department of Science and Technology, India, the Science & Engineering Research Board (SERB), India, the Ministry of Human Resource Development, India, the Spanish Agencia Estatal de Investigación (AEI), the Spanish Ministerio de Ciencia, Innovación y Universidades, the European Union NextGenerationEU/PRTR (PRTR-C17.I1), the ICSC - CentroNazionale di Ricerca in High Performance Computing, Big Dataand Quantum Computing, funded by the European Union NextGenerationEU, the Comunitat Autonòma de les Illes Balears through the Direcció General de Recerca, Innovació i Transformació Digital with funds from the Tourist Stay Tax Law ITS 2017-006, the Conselleria d’Economia, Hisenda i Innovació the FEDER Operational Program 2021–2027 of the Balearic Islands, the Conselleria d’Innovació Universitats, Ciència i Societat Digital de la Generalitat Valenciana and the CERCA Programme Generalitat de Catalunya, Spain, the National Science Centre of Poland and the European Union—European Regional Development Fund; Foundation for Polish Science (FNP), the Polish Ministry of Science and Higher Education, the Swiss National Science Foundation (SNSF), the Russian Science Foundation, the European Commission, the European Social Funds (ESF), the European Regional Development Funds (ERDF), the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, the Hungarian Scientific Research Fund (OTKA), the French Lyon Institute of Origins (LIO), the Belgian Fonds de la Recherche Scientifique (FRS-FNRS), Actions de Recherche Concertées (ARC) and Fonds Wetenschappelijk Onderzoek—Vlaanderen (FWO), Belgium, the Paris Île-de-France Region, the National Research, Development and Innovation Office Hungary (NKFIH), the National Research Foundation of Korea, the Natural Science and Engineering Research Council Canada, Canadian Foundation for Innovation (CFI), the Brazilian Ministry of Science, Technology, and Innovations, the International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR), the Research Grants Council of Hong Kong, the National Natural Science Foundation of China (NSFC), the Leverhulme Trust, the Research Corporation, the National Science and Technology Council (NSTC), Taiwan, the United States Department of Energy, and the Kavli Foundation. The authors gratefully acknowledge the support of the NSF, STFC, INFN, and CNRS for provision of computational resources. This work was supported by MEXT, JSPS Leading-edge Research Infrastructure Program, JSPS Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905: JP17H06358, JP17H06361 and JP17H06364, JSPS Core-to-Core Program A. Advanced Research Networks, JSPS Grant-in-Aid for Scientific Research (S) 17H06133 and 20H05639, JSPS Grant-in-Aid for Transformative Research Areas (A) 20A203: JP20H05854, the joint research program of the Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF), Computing Infrastructure Project of Global Science experimental Data hub Center (GSDC) at KISTI, Korea Astronomy and Space Science Institute (KASI), and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS), AS Grid Center (ASGC) and the National Science and Technology Council (NSTC) in Taiwan under grants including the Rising Star Program and Science Vanguard Research Program, Advanced Technology Center (ATC) of NAOJ, and Mechanical Engineering Center of KEK. We thank the anonymous journal referee for helpful comments.
Software References
Calibration of the LIGO strain data was performed with a GstLAL-based calibration software pipeline (Viets et al. 2018). Data-quality products and event-validation results were computed using the DMT (Zweizig 2006), DQR (LIGO Scientific Collaboration & Virgo Collaboration 2018), DQSEGDB (Fisher et al. 2021), gwdetchar (Urban et al. 2021), hveto (Smith et al. 2011), iDQ (Essick et al. 2020a), Omicron (Robinet et al. 2020), and PythonVirgoTools (Virgo Collaboration 2021) software packages and contributing software tools. Analyses in this catalog relied on software from the LVK Algorithm Library Suite (LIGO Scientific, Virgo, & KAGRA Collaboration 2018). The detection of the signals and subsequent significance evaluations were performed with the GstLAL-based inspiral software pipeline (Messick et al. 2017; Sachdev et al. 2019; Hanna et al. 2020; Cannon et al. 2021), with the MBTA pipeline (Adams et al. 2016; Aubin et al. 2021), and with the PyCBC (Usman et al. 2016; Nitz et al. 2017; Davies et al. 2020) packages. Estimates of the noise spectra and glitch models were obtained using BayesWave (Cornish & Littenberg 2015; Littenberg et al. 2016; Cornish et al. 2021). Low-latency source localization was performed using BAYESTAR (Singer & Price 2016). Source-parameter estimation was primarily performed with the Bilby and Parallel Bilby libraries (Ashton et al. 2019; Romero-Shaw et al. 2020; Smith et al. 2020) using the Dynesty nested sampling package (Speagle 2020). SEOBNRv5PHM waveforms used in parameter estimation were generated using pySEOBNR (Mihaylov et al. 2023). FTI and TIGER waveforms used for testing general relativity were generated using Bilby TGR (Ashton et al. 2024). PESummary was used to post-process and collate parameter estimation results (Hoy & Raymond 2021). The various stages of the parameter estimation analysis were managed with the Asimov library (Williams et al. 2023). Plots were prepared with Matplotlib (Hunter 2007), seaborn (Waskom 2021) and GWpy (Macleod et al. 2021). NumPy (Harris et al. 2020) and SciPy (Virtanen et al. 2020) were used for analyses in the manuscript.
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Additional details
Related works
- Is new version of
- Discussion Paper: arXiv:2404.04248 (arXiv)
- Is supplemented by
- Dataset: 10.7935/6k89-7q62 (DOI)
Dates
- Accepted
-
2024-06-25
- Available
-
2024-07-26Published online