Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog

TLDR: In this article, the population of 47 compact binary mergers detected with a false-alarm rate of 0.614 were dynamically assembled, and the authors found that the BBH rate likely increases with redshift, but not faster than the star formation rate.

Abstract: We report on the population of 47 compact binary mergers detected with a false-alarm rate of 0.01 are dynamically assembled. Third, we estimate merger rates, finding RBBH = 23.9-+8.614.3 Gpc-3 yr-1 for BBHs and RBNS = 320-+240490 Gpc-3 yr-1 for binary neutron stars. We find that the BBH rate likely increases with redshift (85% credibility) but not faster than the star formation rate (86% credibility). Additionally, we examine recent exceptional events in the context of our population models, finding that the asymmetric masses of GW190412 and the high component masses of GW190521 are consistent with our models, but the low secondary mass of GW190814 makes it an outlier.

Figures

Figure 27. Posterior distribution for ceff min , below which we truncate the GAUSSIAN ceff distribution. While the results shown in the main text presume c = -1eff min , in Section D.2, we elevate ceff min to a free hyperparameter to be determined by the data; the resulting marginalized posterior distribution is shown here. In this case, we exclude c 0effmin at 99% credibility. This finding affirms that the signatures of antialigned BH spins are present in our BBH catalog and not a bias due to our choice of parameterized spin model.

Figure 12. Posterior distribution for the fraction of BBH events with positive or negative ceff (corresponding to alignment or antialignment of the BH spin with the orbital angular momentum; see Equation (5)). We also include the fraction of events that are consistent with vanishingly small spins ∣ ∣c < 0.01eff . We confidently infer that a nonzero fraction of events have a positive ceff , which requires at least one component to have a spin tilt less than 90°. A smaller fraction of events have a negative ceff , which requires at least one component with a spin tilt >90°. A nonzero fraction of events have vanishingly small spins. The prior distributions on the parameters are marked with open histograms.

Figure 24. Posterior distribution for spin hyperparameters for DEFAULT, assuming the POWER LAW + PEAK mass model and NONEVOLVING redshift distribution. The fit excludes GW190814. The contours represent 50% and 90% credible bounds. A perfectly aligned spin distribution (s = 0t , z = 1) is ruled out at >99% credibility, consistent with the results of the GAUSSIAN model, but the data disfavor a purely isotropic distribution (z = 0 or s 2t ).

Figure 13. Posterior distribution for the MULTISPIN spin hyperparameters. Each panel corresponds to a different parameter controlling the shape of the spin distribution (see Appendix D.3 for details). Each color corresponds to a different subpopulation (power-law or Gaussian) or component (primary or secondary mass) of the binary. The power-law subpopulation is slightly better measured than the Gaussian component, as a large number of detections are assigned to it. The results hint at a potential correlation between mass and spin, but the large measurement errors mean the spin distributions are consistent between the two subpopulations.

Figure 8. (a) Posterior distribution for the minimum mass parameter in the TRUNCATED (navy), BROKEN POWER-LAW (green), and POWER LAW + PEAK (blue) models. The solid lines show the posterior for mmin when fitting the models to the confident BBH events excluding GW190814, while the dashed lines show the fits to the BBH events including GW190814. The TRUNCATEDMODEL is disfavored. (b) Distribution of primary masses inferred using the POWER LAW+ PEAK model when including (navy dashed line) and excluding (light blue solid line) GW190814. Only the secondary mass of GW190814 is below M3 . However, the primary and secondary mass distributions share a common mmin parameter in all models we consider. The effect of GW190814 on the mass spectrum at low masses inferred using the BROKEN POWER-LAW model is similar.

Table 6 The Parameters that Describe the BBH Mass Distribution for Model POWER LAW + PEAK

Full text

Louisiana State University Louisiana State University
LSU Digital Commons LSU Digital Commons
Faculty Publications Department of Physics & Astronomy
5-20-2021
Population properties of compact objects from the second LIGO-Population properties of compact objects from the second LIGO-
Virgo gravitational-wave transient catalog Virgo gravitational-wave transient catalog
R. Abbott
California Institute of Technology
T. D. Abbott
Louisiana State University
S. Abraham
Inter-University Centre for Astronomy and Astrophysics India
F. Acernese
Università degli Studi di Salerno
K. Ackley
Monash University
See next page for additional authors
Follow this and additional works at: https://digitalcommons.lsu.edu/physics_astronomy_pubs
Recommended Citation Recommended Citation
Abbott, R., Abbott, T., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R., Adya, V.,
Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O., Aiello, L., Ain, A., Ajith, P., Allen, G., Allocca,
A., Altin, P., Amato, A., Anand, S., Ananyeva, A., Anderson, S., Anderson, W., Angelova, S., Ansoldi, S.,
Antelis, J., Antier, S., Appert, S., Arai, K., Araya, M., & Areeda, J. (2021). Population properties of compact
objects from the second LIGO-Virgo gravitational-wave transient catalog.
Astrophysical Journal Letters,
913
(1) https://doi.org/10.3847/2041-8213/abe949
This Article is brought to you for free and open access by the Department of Physics & Astronomy at LSU Digital
Commons. It has been accepted for inclusion in Faculty Publications by an authorized administrator of LSU Digital
Commons. For more information, please contact ir@lsu.edu.

Authors Authors
R. Abbott, T. D. Abbott, S. Abraham, F. Acernese, K. Ackley, A. Adams, C. Adams, R. X. Adhikari, V. B. Adya,
C. Affeldt, M. Agathos, K. Agatsuma, N. Aggarwal, O. D. Aguiar, L. Aiello, A. Ain, P. Ajith, G. Allen, A. Allocca,
P. A. Altin, A. Amato, S. Anand, A. Ananyeva, S. B. Anderson, W. G. Anderson, S. V. Angelova, S. Ansoldi, J.
M. Antelis, S. Antier, S. Appert, K. Arai, M. C. Araya, and J. S. Areeda
This article is available at LSU Digital Commons: https://digitalcommons.lsu.edu/physics_astronomy_pubs/986

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Population Properties of Compact Objects from the Second LIGO–Virgo
Gravitational-Wave Transient Catalog
R. Abbott
1
, T. D. Abbott
2
, S. Abraham
3
, F. Acernese
4,5
, K. Ackley
6
, A. Adams
7
, C. Adams
8
, R. X. Adhikari
1
, V. B. Adya
9
,
C. Affeldt
10,11
, M. Agathos
12,13
, K. Agatsuma
14
, N. Aggarwal
15
, O. D. Aguiar
16
, L. Aiello
17,18
, A. Ain
19,20
, P. Ajith
21
,
G. Allen
22
, A. Allocca
19
, P. A. Altin
9
, A. Amato
23
, S. Anand
1
, A. Ananyeva
1
, S. B. Anderson
1
, W. G. Anderson
24
,
S. V. Angelova
25
, S. Ansoldi
26,27
, J. M. Antelis
28
, S. Antier
29
, S. Appert
1
, K. Arai
1
, M. C. Araya
1
, J. S. Areeda
30
, M. Arène
29
,
N. Arnaud
31,32
, S. M. Aronson
33
, K. G. Arun
34
, Y. Asali
35
, S. Ascenzi
17,36
, G. Ashton
6
, S. M. Aston
8
, P. Astone
37
, F. Aubin
38
,
P. Aufmuth
10,11
, K. AultONeal
28
, C. Austin
2
, V. Avendano
39
, S. Babak
29
, F. Badaracco
17,18
, M. K. M. Bader
40
, S. Bae
41
,
A. M. Baer
7
, S. Bagnasco
42
, J. Baird
29
, M. Ball
43
, G. Ballardin
32
, S. W. Ballmer
44
, A. Bals
28
, A. Balsamo
7
, G. Baltus
45
,
S. Banagiri
46
, D. Bankar
3
, R. S. Bankar
3
, J. C. Barayoga
1
, C. Barbieri
47,48,49
, B. C. Barish
1
, D. Barker
50
, P. Barneo
51
,
S. Barnum
52
, F. Barone
5,53
, B. Barr
54
, L. Barsotti
52
, M. Barsuglia
29
, D. Barta
55
, J. Bartlett
50
, I. Bartos
33
, R. Bassiri
56
,
A. Basti
19,20
, M. Bawaj
57,58
, J. C. Bayley
54
, M. Bazzan
59,60
, B. R. Becher
61
, B. Bécsy
62
, V. M. Bedakihale
63
, M. Bejger
64
,
I. Belahcene
31
, D. Beniwal
65
, M. G. Benjamin
28
, T. F. Bennett
66
, J. D. Bentley
14
, F. Bergamin
10,11
, B. K. Berger
56
,
G. Bergmann
10,11
, S. Bernuzzi
13
, C. P. L. Berry
15
, D. Bersanetti
67
, A. Bertolini
40
, J. Betzwieser
8
, R. Bhandare
68
, A. V. Bhandari
3
,
D. Bhattacharjee
69
, J. Bidler
30
, I. A. Bilenko
70
, G. Billingsley
1
, R. Birney
71
, O. Birnholtz
72
, S. Biscans
1,52
, M. Bischi
73,74
,
S. Biscoveanu
52
, A. Bisht
10,11
, M. Bitossi
19,32
, M.-A. Bizouard
75
, J. K. Blackburn
1
, J. Blackman
76
, C. D. Blair
77
, D. G. Blair
77
,
R. M. Blair
50
, O. Blanch
78
, F. Bobba
79,80
, N. Bode
10,11
, M. Boer
75
, Y. Boetzel
81
, G. Bogaert
75
, M. Boldrini
37,82
, F. Bondu
83
,
E. Bonilla
56
, R. Bonnand
38
, P. Booker
10,11
, B. A. Boom
40
, R. Bork
1
, V. Boschi
19
, S. Bose
3
, V. Bossilkov
77
, V. Boudart
45
,
Y. Bouffanais
59,60
, A. Bozzi
32
, C. Bradaschia
19
, P. R. Brady
24
, A. Bramley
8
, M. Branchesi
17,18
, J. E. Brau
43
, M. Breschi
13
,
T. Briant
84
, J. H. Briggs
54
, F. Brighenti
73,74
, A. Brillet
75
, M. Brinkmann
10,11
, P. Brockill
24
, A. F. Brooks
1
, J. Brooks
32
,
D. D. Brown
65
, S. Brunett
1
, G. Bruno
85
, R. Bruntz
7
, A. Buikema
52
, T. Bulik
86
, H. J. Bulten
40,87
, A. Buonanno
88,89
,
R. Buscicchio
14
, D. Buskulic
38
, R. L. Byer
56
, M. Cabero
10,11
, L. Cadonati
90
, M. Caesar
91
, G. Cagnoli
23
, C. Cahillane
1
,
J. Calderón Bustillo
6
, J. D. Callaghan
54
, T. A. Callister
92
, E. Calloni
5,93
, J. B. Camp
94
, M. Canepa
67,95
, K. C. Cannon
96
, H. Cao
65
,
J. Cao
97
, G. Carapella
79,80
, F. Carbognani
32
, M. F. Carney
15
, M. Carpinelli
98,99
, G. Carullo
19,20
, T. L. Carver
100
,
J. Casanueva Diaz
32
, C. Casentini
36,101
, S. Caudill
40
, M. Cavaglià
69
, F. Cavalier
31
, R. Cavalieri
32
, G. Cella
19
, P. Cerdá-Durán
102
,
E. Cesarini
36
, W. Chaibi
75
, K. Chakravarti
3
, C.-L. Chan
103
, C. Chan
96
, K. Chandra
104
, P. Chanial
32
, S. Chao
105
, P. Charlton
106
,
E. A. Chase
15
, E. Chassande-Mottin
29
, D. Chatterjee
24
, D. Chattopadhyay
107
, M. Chaturvedi
68
, K. Chatziioannou
92
, A. Chen
103
,
H. Y. Chen
108
, X. Chen
77
, Y. Chen
76
, H.-P. Cheng
33
, C. K. Cheong
103
, H. Y. Chia
33
, F. Chiadini
80,109
, R. Chierici
110
,
A. Chincarini
67
, A. Chiummo
32
, G. Cho
111
,H.S.Cho
112
, M. Cho
89
, S. Choate
91
, N. Christensen
75
, Q. Chu
77
, S. Chua
84
,
K. W. Chung
113
, S. Chung
77
, G. Ciani
59,60
, P. Ciecielag
64
, M. Cieślar
64
, M. Cifaldi
36,101
, A. A. Ciobanu
65
, R. Ciolfi
60,114
,
F. Cipriano
75
, A. Cirone
67,95
, F. Clara
50
, E. N. Clark
115
, J. A. Clark
90
, L. Clarke
116
, P. Clearwater
117
, S. Clesse
85
, F. Cleva
75
,
E. Coccia
17,18
, P.-F. Cohadon
84
, D. E. Cohen
31
, M. Colleoni
118
, C. G. Collette
119
, C. Collins
14
, M. Colpi
47,48
, M. Constancio, Jr.
16
,
L. Conti
60
, S. J. Cooper
14
, P. Corban
8
, T. R. Corbitt
2
, I. Cordero-Carrión
120
, S. Corezzi
57,58
, K. R. Corley
35
, N. Cornish
62
,
D. Corre
31
, A. Corsi
121
, S. Cortese
32
, C. A. Costa
16
, R. Cotesta
88
, M. W. Coughlin
1,46
, S. B. Coughlin
15,100
, J.-P. Coulon
75
,
S. T. Countryman
35
, P. Couvares
1
, P. B. Covas
118
, D. M. Coward
77
, M. J. Cowart
8
, D. C. Coyne
1
, R. Coyne
122
,
J. D. E. Creighton
24
, T. D. Creighton
123
, M. Croquette
84
, S. G. Crowder
124
, J. R. Cudell
45
, T. J. Cullen
2
, A. Cumming
54
,
R. Cummings
54
, L. Cunningham
54
, E. Cuoco
32,125
, M. Curylo
86
, T. Dal Canton
31,88
, G. Dálya
126
, A. Dana
56
,
L. M. DaneshgaranBajastani
66
,B.D’Angelo
67,95
, S. L. Danilishin
127
,S.D’Antonio
36
, K. Danzmann
10,11
, C. Darsow-Fromm
128
,
A. Dasgupta
63
, L. E. H. Datrier
54
, V. Dattilo
32
, I. Dave
68
, M. Davier
31
, G. S. Davies
129
, D. Davis
1
, E. J. Daw
130
, R. Dean
91
,
D. DeBra
56
, M. Deenadayalan
3
, J. Degallaix
131
, M. De Laurentis
5,93
, S. Deléglise
84
, V. Del Favero
132
, F. De Lillo
85
, N. De Lillo
54
,
W. Del Pozzo
19,20
, L. M. DeMarchi
15
, F. De Matteis
36,101
,V.D’Emilio
100
, N. Demos
52
, T. Denker
10,11
, T. Dent
129
, A. Depasse
85
,
R. De Pietri
133,134
, R. De Rosa
5,93
, C. De Rossi
32
, R. DeSalvo
80,135
, O. de Varona
10,11
, S. Dhurandhar
3
, M. C. Díaz
123
,
M. Diaz-Ortiz, Jr.
33
, N. A. Didio
44
, T. Dietrich
40
, L. Di Fiore
5
, C. DiFronzo
14
, C. Di Giorgio
79,80
, F. Di Giovanni
102
,
M. Di Giovanni
136,137
, T. Di Girolamo
5,93
, A. Di Lieto
19,20
, B. Ding
119
, S. Di Pace
37,82
, I. Di Palma
37,82
, F. Di Renzo
19,20
,
A. K. Divakarla
33
, A. Dmitriev
14
, Z. Doctor
43
,L.D’Onofrio
5,93
, F. Donovan
52
, K. L. Dooley
100
, S. Doravari
3
, I. Dorrington
100
,
T. P. Downes
24
, M. Drago
17,18
, J. C. Driggers
50
,Z.Du
97
, J.-G. Ducoin
31
, P. Dupej
54
, O. Durante
79,80
,D.D’Urso
98,99
,
P.-A. Duverne
31
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50
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6
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54
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43
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130
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138
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8
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9
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S. S. Eikenberry
33
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38
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52
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100
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5,93
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108
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118
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38
,
Z. B. Etienne
139
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1
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8
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140
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17,36,101
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100
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A. M. Farah
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43
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92,141
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100
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39
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45,130
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,
J. Feicht
1
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56
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29
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55,143
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90
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52
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19,20
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16
,
F. Fidecaro
19,20
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86
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32
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17,18
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108
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7
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52
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80,144
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M. Fitz-Axen
46
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80,145
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38,146
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46
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30
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96
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102,147
, P. W. F. Forsyth
9
,
J.-D. Fournier
75
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37,82
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19
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126
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14
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43
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31
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52
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8
,
https://doi.org/10.3847/2041-8213/abe949
The Astrophysical Journal Letters, 913:L7 (41pp), 2021 May 20
© 2021 The Author(s). Published by The American Astronomical Society.
1
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Frequently asked questions

Q1. What have the authors contributed in "Population properties of compact objects from the second ligo-virgo gravitational-wave transient catalog" ?

The authors report on the population of 47 compact binary mergers detected with a false-alarm rate of < 1 yr 1 in the second LIGO–Virgo Gravitational-WaveTransientCatalog. 

Q2. What are the future works in "Population properties of compact objects from the second ligo-virgo gravitational-wave transient catalog" ?

4. the authors detect clear evidence of spin-induced, general relativistic precession of the orbital plane. As future observations subject their models to increasing scrutiny, it is inevitable that refinements will be required to fit newly resolved features. 

Q3. What is the spin-tilt distribution from Talbot & Thrane?

The spin-tilt distribution from Talbot & Thrane (2017) is a mixture model comprising two components: an isotropic component designed to model dynamically assembled binaries and a component in which the spins are preferentially aligned with the orbital angular momentum, as expected for isolated field binaries. 

Q4. What is the probability of correlations between the spins and masses of BBH systems?

If hierarchical mergers are present in GWTC-2, then one may expect correlations between the spins and masses of BBH systems, with more massive hierarchical mergers also possessing larger spins. 

Q5. What is the significance of the posteriors for BBHs with negative effective inspiral?

The presence of BBH systems with negative effectiveinspiral spin parameters carries implications for the formation channels that give rise to stellar-mass BBH mergers. 

Q6. What is the effect of selection on the posterior of the graph?

Selection effects can, however, only decrease the efficiency with which events with large inplane spins are detected; incorporating such effects would further shift the posterior in Figure 9 away from z = 1 and s = 0t and/or more strongly rule out a delta function at c = 0p . 

Q7. What was the envisioned power law component of the POWER LAW + PEAK model?

It was envisioned (Talbot & Thrane 2018) that the power-law component of the POWER LAW + PEAK model would terminate in the vicinity of this peak to create a high-mass gap. 

Q8. What is the significance of the results of the GWTC-2 analysis?

Since their statistical framework relies on accurately quantifying the selection effects of their search, the authors only include events identified in GWTC-2, for which the authors have measured the search sensitivity; see Appendix A. 

Q9. Why is the posterior predictive distribution skew to much higher masses?

because of selection effects, the posterior predictive distribution skews to much higher masses, as seen in Figure 4, so that the probability of detecting at least one event with m M801 after observing 44 BBH events drawn from the POWER LAW + PEAK posterior predictive distribution of Figure 4 is high: 32%. 

Q10. What is the important explanation of negative effective inspiral spin parameters?

As mentioned above, dynamical formation in dense clustersis not the only astrophysical explanation of negative effective inspiral spin parameters. 

Q11. How many posterior samples are there in Figure 10?

In particular, when measuring the mean mp and standard deviation sp of the cp distribution, the case m s= = 0p p is ruled out at>99% credibility; fewer than 1% of posterior samples occur at m 0.05p and s 0.05p . 

Q12. How does the model compare to the submodel?

For the POWER LAW + PEAK model, which includes a fraction lpeak of systems in the Gaussian component, the authors compare the submodel with l = 0peak . 

Q13. Why does the fit with GW190814 overestimate the rate of systems with masses?

because their mass distribution models do not extrapolate well to <m M32 (see Section 5.1), the fit with GW190814 likely overestimates the rate of systems with masses between ∼2.6 and ∼ M6 .