Equation (2) is to extend the search to lower $M_{ej}$ and $\log X_{\text{lan}}$ and thus lower luminosities. Our method of accounting for uncertainty in the KN population is driving our results. If we adopt the Coughlin et al. (2018) model parameters and uncertainties, our detection efficiencies will increase and exposure times decrease. Using the GW170817 absolute magnitude for detection is likely overly optimistic, as suggested by Colombo et al. (2022) placing GW170817 at the 75th percentile bright and Setzer et al. (2023) placing it at the 95th percentile bright. The studies using 1 m class telescopes are most likely to assume GW170817 analogs, though Petrov et al. (2022) uses both that and a lower-luminosity red model, and Sagués Carracedo et al. (2021) does a careful analysis of viewing angle-dependent models. The studies assuming the Rubin Observatory (Cowperthwaite et al. 2019; Chen et al. 2021) or a variety/network of telescopes (Coughlin et al. 2020; Chase et al. 2022) tend to analyze detection probabilities for lower-luminosity events. In summary, one will have to be careful comparing our detection probabilities with others in the literature, which often use a single luminous model or evaluate and average detection efficiencies over a grid of models using a uniform prior. In our language, “chance of finding it” in Figure 4 is to be interpreted as “fraction of models detected given our priors on the space of models.”

## 7.1. Applicability to NS-BH and Mass Gap Events

For standard siren studies, NS-BH mergers are just as valuable as BNS mergers, as long as they produce EM counterparts. NS-BH merger events have higher distances for similar S/N than BNS mergers, as can be seen in Table 8.

The dominant factor for use in standard siren cosmology is the probability of a KN given an NS-BH merger. No counterpart to an NS-BH merger has been detected (e.g., Kawaguchi et al. 2020a; Morgan et al. 2020; Anand et al. 2021). Zhu et al. (2021) argue that no detectable KN counterpart was expected for NS-BH mergers in O3. Kawaguchi et al. (2020b) and Darbha et al. (2021) study the brightness of KNe from NS-BH mergers, and Drozda et al. (2020) does the same for mass gap objects. The summary is that only a fraction of NS-BH events will produce KNe, primarily those mergers with low mass ratios and high spin.

The Kawaguchi et al. (2020b) models have absolute magnitudes that peak for *r* and *i* at −14.5 and −15.0, respectively. There is a spread of about 1 mag fainter in the *i*-band absolute magnitude, going fainter as the binary mass ratio increases and the effective spin gets smaller. Our red and faint model has an *i*-band absolute magnitude of −14.5, midway through the range of Kawaguchi et al. (2020b). Petrov et al. (2022) adopt the Bulla (2019) models, broken into BNS and NS-BH models both optimistic ($M_{ej} = 0.05, 0.08$) and conservative ($M_{ej} = 0.01, 0.01$). The optimistic BNS model has an absolute magnitude in the *r* band of −16.0, and the conservative NS-BH model has −14.8; these correspond well to our bright and blue and red and faint models, respectively. Taking our red and faint model as appropriate for dynamical ejecta-dominated NS-BH mergers, our low-TT strategy has a 50% detection probability out to 330 Mpc (see Figure 4). Our strategies are sufficient to obtain the majority of NS-BH events that have EM counterparts if they are at distances ≪330 Mpc.

<table><caption>**Table 8**
LVC GWTC Events (O1–O3) Containing NSs and with $M_2 < 5 M_\odot$ and $S/N > 8$</caption><thead><tr><th>ID</th><th><i>M</i><sub>1</sub><br>(<i>M</i><sub>☉</sub>)</th><th><i>M</i><sub>2</sub><br>(<i>M</i><sub>☉</sub>)</th><th>Distance<br>(Mpc)</th><th>S/N</th><th>Class</th></tr></thead><tbody><tr><td>GW170817</td><td>1.5</td><td>1.3</td><td>40</td><td>33</td><td>BNS</td></tr><tr><td>GW190425</td><td>2.0</td><td>1.4</td><td>160</td><td>13</td><td>BNS</td></tr><tr><td>GW190814</td><td>23.2</td><td>2.6</td><td>240</td><td>22</td><td>BH-NS</td></tr><tr><td>GW200105_162426*</td><td>9.0</td><td>1.9</td><td>270</td><td>14</td><td>BH-NS</td></tr><tr><td>GW200115_042309</td><td>5.9</td><td>1.4</td><td>290</td><td>11</td><td>BH-NS</td></tr><tr><td>GW190426_152155</td><td>5.7</td><td>1.5</td><td>370</td><td>10</td><td>BH-NS</td></tr><tr><td>GW191219_163120</td><td>31.1</td><td>1.2</td><td>550</td><td>9</td><td>BH-NS</td></tr><tr><td>GW190917_114630</td><td>9.3</td><td>2.1</td><td>720</td><td>10</td><td>BH-NS</td></tr><tr><td>GW200210_092254</td><td>24.1</td><td>2.8</td><td>940</td><td>8</td><td>BH-NS</td></tr></tbody></table>

Note. S/N is the matched filter S/N. GW200105_162426* has $p_{astro} = 0.3$ and is thus considered a marginal candidate. Only two events have a 90% confidence sky area of <30 deg$^2$, two at <300 deg$^2$, and two at <400 deg$^2$. The median sky area is 1700 deg$^2$. Data are from https://www.gw-openscience.org/eventapi/html/allevents/.

## 7.2. Blanco/DECam and Rubin LSST

It is of interest to compare the strategies defined here with the program outlined in Chen et al. (2021). They assume inclination independence and a GW170817-like KN and argue for two filter observations. The program conservatively assumes 30% of A+ events by dedicating 7 hr of Rubin Observatory time in 30 s exposures, capturing $12 \text{ yr}^{-1}$. Our expectation is that the Rubin ToO program will use 3% of the available LSST time, so on order of 100 hr, which can pursue all BNS events in LVK O4 assuming $1 \text{ month}^{-1}$ and 8 hr event$^{-1}$, so pursuing light curves. Alternatively and more likely, Rubin will choose to observe the 50% best events by sky area in both the BNS and the NS-BH categories. In this scenario, a good use of DECam/Blanco would be to follow up the others that have sky area <300 deg$^2$. Table 5 suggests that this would be viable.

In fact, if the results of Petrov et al. (2022) hold, then there will be many merger events containing NSs that have sky areas greater than 300 deg$^2$; our simulations would have to be extended by another ~360 events, all with sky area >300 deg$^2$, to match their statistics. For the bright siren cosmology, every NS event is important. We demonstrate here that the Blanco/DECam, especially in combination with the Zwicky observatory and its counterparts PS1, OAJ, and LS4, is capable of following up the sources with sky area <300 deg$^2$. The optimal use of the Vera C. Rubin Observatory, with its immense endowment, is to follow up the LVK sources with >300 deg$^2$. The combination of sky coverage and depth is unmatched. Petrov et al. (2022) predicts that the median sky coverage for BNS events in O4 is $1820_{-170}^{+190}$ deg$^2$, the median luminosity distance is $352 \pm 10$ deg$^2$, and the NS-BH median distance is further away. For the Rubin field of view of 9.6 deg$^2$, the number of exposures to cover the sky area once is ≈200, which at 100 s exposures can be done in less than 6 hr, assuming a reddish and slow model and 1.2 mag deeper $m_0$ for Equation (1). Likely one could build a two-visit strategy that would take 10 hr per event, allowing Rubin to follow up 10 additional events per year without light curves. The Rubin time-domain ecosystem of data, brokers, and routine spectroscopic follow-up is likely to minimize positives, though perhaps not until after O4.
As discussed in Morgan et al. (2020), Garcia et al. (2020), and Tucker et al. (2022), the need for coordination with spectroscopic telescopes is vital in identifying the true counterpart. Given that there has only been one confirmed optical counterpart, there is uncertainty in the expected light curve from photometric data.

## 8. Conclusion

In this paper, we create families of observing strategies that optimize the probability of detecting a KN within DECam's images. We examine various filter choices, depths, area coverage, and cadence of observations in order to ensure an optimal chance of detection. Given the expanded range of sensitivity in future LVK observing runs, deeper exposures will be necessary in order to be sensitive to the quickly fading counterpart. As we do not have unlimited time for such follow-ups, we examine how we can optimize our chance of detection while taking into account real-world constraints.

We chose to optimize our strategies based on the probability of detecting the KN within two images that are at least 30 minutes apart. This constraint is put in place in order to help eliminate asteroids and other sources of noise. We explore two different types of observing scenarios. The first is a homogeneous covering of the sky area with a single exposure time, and the second uses deeper exposures in the higher-probability sky areas and shallower exposures in the rest of the area. We then categorize our strategies by observational constraint, where each family of strategies is taken from the top 10% or 5% of top strategies. The top strategies use all available resources and are useful as a benchmark for the full detection capability of the DECam.

Examining each of the realistic observing scenarios, we find we can achieve ~75%-80% probability of detection out to 190 Mpc (the nominal limit of the LVK BNS range) for a wide range of KN parameters (reddish and slow), ~65% for a fainter and redder KN (red and faint), and over 90% for a bright and blue model along the full range of distances limited to 330 Mpc. Additionally, we provide the mean detection probability and total telescope time required for detection and confirmation in each KN model for a given range of GW event areas and distances in Figure 6. In particular, this plot might be used as a guide to how likely it is to succeed in KN detection of specific future events considering a trade-off between time budget and optimal chances.

While DECam will continue to be the optimal camera in the southern hemisphere during the next observing run, efforts to detect the next KN optical counterpart will be greatly aided by other telescopes that are planned to be online during this time, for example, the expected addition of the Simonyi Telescope at the Vera Rubin Observatory.

## Acknowledgments

Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State

University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey.

The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NFS's NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium.

Based in part on observations at Cerro Tololo Inter-American Observatory at NSF's NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.

The DES data management system is supported by the National Science Foundation under grant Nos. AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013), including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2).

This manuscript has been authored by the Fermi Research Alliance, LLC, under contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under grant No. 1744555. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This paper has gone through internal review by the DES collaboration. C.R.B. acknowledges financial support from CNPq (316072/2021-4), FAPERJ (grants 201.456/2022 and 210.330/2022) and FINEP contract 01.22.0505.00 (ref. 1891/22).
As discussed in Morgan et al. (2020), Garcia et al. (2020), and Tucker et al. (2022), the need for coordination with spectroscopic telescopes is vital in identifying the true counterpart. Given that there has only been one confirmed optical counterpart, there is uncertainty in the expected light curve from photometric data.

## 8. Conclusion

In this paper, we create families of observing strategies that optimize the probability of detecting a KN within DECam's images. We examine various filter choices, depths, area coverage, and cadence of observations in order to ensure an optimal chance of detection. Given the expanded range of sensitivity in future LVK observing runs, deeper exposures will be necessary in order to be sensitive to the quickly fading counterpart. As we do not have unlimited time for such follow-ups, we examine how we can optimize our chance of detection while taking into account real-world constraints.

We chose to optimize our strategies based on the probability of detecting the KN within two images that are at least 30 minutes apart. This constraint is put in place in order to help eliminate asteroids and other sources of noise. We explore two different types of observing scenarios. The first is a homogeneous covering of the sky area with a single exposure time, and the second uses deeper exposures in the higher-probability sky areas and shallower exposures in the rest of the area. We then categorize our strategies by observational constraint, where each family of strategies is taken from the top 10% or 5% of top strategies. The top strategies use all available resources and are useful as a benchmark for the full detection capability of the DECam.

Examining each of the realistic observing scenarios, we find we can achieve ~75%-80% probability of detection out to 190 Mpc (the nominal limit of the LVK BNS range) for a wide range of KN parameters (reddish and slow), ~65% for a fainter and redder KN (red and faint), and over 90% for a bright and blue model along the full range of distances limited to 330 Mpc. Additionally, we provide the mean detection probability and total telescope time required for detection and confirmation in each KN model for a given range of GW event areas and distances in Figure 6. In particular, this plot might be used as a guide to how likely it is to succeed in KN detection of specific future events considering a trade-off between time budget and optimal chances.

While DECam will continue to be the optimal camera in the southern hemisphere during the next observing run, efforts to detect the next KN optical counterpart will be greatly aided by other telescopes that are planned to be online during this time, for example, the expected addition of the Simonyi Telescope at the Vera Rubin Observatory.

## Acknowledgments

Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State

University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey.

The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NFS's NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium.

Based in part on observations at Cerro Tololo Inter-American Observatory at NSF's NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.

The DES data management system is supported by the National Science Foundation under grant Nos. AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013), including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2).

This manuscript has been authored by the Fermi Research Alliance, LLC, under contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under grant No. 1744555. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This paper has gone through internal review by the DES collaboration. C.R.B. acknowledges financial support from CNPq (316072/2021-4), FAPERJ (grants 201.456/2022 and 210.330/2022) and FINEP contract 01.22.0505.00 (ref. 1891/22).
R.M. thanks the LSSTC Data Science Fellowship Program, which is funded by LSSTC; NSF Cybertraining grant No. 1829740; the Brinson Foundation; and the Moore Foundation.

A.P. acknowledges that support for this work was provided by NASA through NASA Hubble Fellowship grant HST-HF2-51488.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555.

L.S.-S. acknowledges financial support from FAPESP through grant No. 2020/03301-5.

MESP is funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) under Germany's Excellence Strategy—EXC 2121 “Quantum Universe”—390833306.

F.O.E. acknowledges financial support from FONDECYT grant No. 1201223.

The authors made use of Sci-Mind servers machines developed by the CBPF AI LAB team and would like to thank P. Russano and M. Portes de Albuquerque for all the support in infrastructure matters.

## ORCID iDs

C. R. Bom https://orcid.org/0000-0003-4383-2969

J. Annis https://orcid.org/0000-0002-0609-3987

A. Garcia https://orcid.org/0000-0001-9578-6322

A. Palmese https://orcid.org/0000-0002-6011-0530

N. Sherman https://orcid.org/0000-0001-5399-0114

M. Soares-Santos https://orcid.org/0000-0001-6082-8529

L. Santana-Silva https://orcid.org/0000-0003-3402-6164

R. Morgan https://orcid.org/0000-0002-7016-5471

K. Bechtol https://orcid.org/0000-0001-8156-0429

T. Davis https://orcid.org/0000-0003-4932-9379

H. T. Diehl https://orcid.org/0000-0002-8357-7467

S. S. Allam https://orcid.org/0000-0002-7069-7857

T. G. Bachmann https://orcid.org/0000-0002-6119-5353

J. García-Bellido https://orcid.org/0000-0002-9370-8360

M. S. S. Gill https://orcid.org/0000-0003-2524-5154

K. Herner https://orcid.org/0000-0001-6718-2978

M. Makler https://orcid.org/0000-0003-2206-2651

F. Olivares E. https://orcid.org/0000-0002-5115-6377

M. E. S. Pereira https://orcid.org/0000-0002-7131-7684

D. L. Tucker https://orcid.org/0000-0001-7211-5729

M. P. Wiesner https://orcid.org/0000-0001-8653-7738

M. Aguena https://orcid.org/0000-0001-5679-6747

O. Alves https://orcid.org/0000-0002-7394-9466

D. Bacon https://orcid.org/0000-0002-2562-8537

P. H. Bernardinelli https://orcid.org/0000-0003-0743-9422

S. Bocquet https://orcid.org/0000-0002-4900-805X

D. Brooks https://orcid.org/0000-0002-8458-5047

M. Carrasco Kind https://orcid.org/0000-0002-4802-3194

J. Carretero https://orcid.org/0000-0002-3130-0204

C. Conselice https://orcid.org/0000-0003-1949-7638

J. De Vicente https://orcid.org/0000-0001-8318-6813

S. Desai https://orcid.org/0000-0002-0466-3288

S. Everett https://orcid.org/0000-0002-3745-2882

I. Ferrero https://orcid.org/0000-0002-1295-1132

J. Frieman https://orcid.org/0000-0003-4079-3263

D. W. Gerdes https://orcid.org/0000-0001-6942-2736

D. Gruen https://orcid.org/0000-0003-3270-7644

R. A. Gruendl https://orcid.org/0000-0002-4588-6517

G. Gutierrez https://orcid.org/0000-0003-0825-0517

S. R. Hinton https://orcid.org/0000-0003-2071-9349

D. L. Hollowood https://orcid.org/0000-0002-9369-4157

K. Honscheid https://orcid.org/0000-0002-6550-2023

D. J. James https://orcid.org/0000-0001-5160-4486

K. Kuehn https://orcid.org/0000-0003-0120-0808

N. Kuropatkin https://orcid.org/0000-0003-2511-0946

P. Melchior https://orcid.org/0000-0002-8873-5065

J. Mena-Fernández https://orcid.org/0000-0001-9497-7266

F. Menteau https://orcid.org/0000-0002-1372-2534

A. Pieres https://orcid.org/0000-0001-9186-6042

A. A. Plazas Malagón https://orcid.org/0000-0002-2598-0514

E. Sanchez https://orcid.org/0000-0002-9646-8198

I. Sevilla-Noarbe https://orcid.org/0000-0002-1831-1953

M. Smith https://orcid.org/0000-0002-3321-1432

E. Suchyta https://orcid.org/0000-0002-7047-9358

G. Tarle https://orcid.org/0000-0003-1704-0781

C. To https://orcid.org/0000-0001-7836-2261

N. Weaverdyck https://orcid.org/0000-0001-9382-5199

## References

Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017a, ApJ, 848, L13

Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017b, ApJ, 848, L12

Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2018, LRR, 21, 3

Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2020, LRR, 23, 3

Abbott, R., Abbott, T. D., Abraham, S., et al. 2021, ApJ, 915, L5

Abbott, R., Abbott, T. D., Acernese, F., et al. 2023, PhRvX, 13, 011048

Abdalla, E., Abellán, G. F., Aboubrahim, A., et al. 2022, JHEAp, 34, 49

Anand, S., Coughlin, M. W., Kasliwal, M. M., et al. 2021, NatAs, 5, 46

Andreoni, I., Ackley, K., Cooke, J., et al. 2017, PASA, 34, e069

Andreoni, I., Goldstein, D. A., Anand, S., et al. 2019, ApJ, 881, L16

Andreoni, I., Goldstein, D. A., Kasliwal, M. M., et al. 2020, ApJ, 890, 131

Arcavi, I., Hosseinzadeh, G., Howell, D. A., et al. 2017, Natur, 551, 64

Ashton, G., Ackley, K., Magaña Hernandez, I., & Piotrzkowski, B. 2021, CQGra, 38, 235004

Balasubramanian, A., Corsi, A., Mooley, K. P., et al. 2021, ApJ, 914, L20

Bom, C. R., & Palmese, A. 2023, arXiv:2307.01330

Bulla, M. 2019, MNRAS, 489, 5037

Buonanno, A., Iyer, B. R., Ochsner, E., Pan, Y., & Sathyaprakash, B. S. 2009, PhRvD, 80, 084043

Chase, E. A., O'Connor, B., Fryer, C. L., et al. 2022, ApJ, 927, 163

Chen, H.-Y., Cowperthwaite, P. S., Metzger, B. D., & Berger, E. 2021, ApJ, 908, L4

Chen, H.-Y., Fishbach, M., & Holz, D. E. 2018, Natur, 562, 545

Chen, H.-Y., & Holz, D. E. 2014, arXiv:1409.0522

Colombo, A., Salafia, O. S., Gabrielli, F., et al. 2022, ApJ, 937, 79

Coughlin, M. W., Dietrich, T., Antier, S., et al. 2020, MNRAS, 497, 1181

Coughlin, M. W., Dietrich, T., Doctor, Z., et al. 2019, MNRAS, 480, 3871

Coughlin, M. W., Dietrich, T., Margalit, B., & Metzger, B. D. 2019, MNRAS, 489, L91

Coulter, D., Foley, R., Kilpatrick, C., et al. 2017, Sci, 358, 1556

Cowperthwaite, P., Berger, E., Villar, V., et al. 2017, ApJ, 848, L17

Cowperthwaite, P. S., Villar, V. A., Scolnic, D. M., & Berger, E. 2019, ApJ, 874, 88

Darbha, S., & Kasen, D. 2020, ApJ, 897, 150

Darbha, S., Kasen, D., Foucart, F., & Price, D. J. 2021, ApJ, 915, 69

Del Pozzo, W. 2012, PhRvD, 86, 043011

Díaz, M. C., Macri, L. M., Garcia Lambas, D., et al. 2017, ApJ, 848, L29

Dietrich, T., & Ujevic, M. 2017, CQGra, 34, 105014

Drout, M. R., Piro, A. L., Shappee, B. J., et al. 2017, Sci, 358, 1570

Drozda, P., Belczynski, K., O'Shaughnessy, R., Bulik, T., & Fryer, C. L. 2022, A&A, 667, A126

Finke, A., Foffa, S., Iacovelli, F., Maggiore, M., & Mancarella, M. 2021, JCAP, 2021, 026

Finn, L. S., & Chernoff, D. F. 1993, PhRvD, 47, 2198

Flaugher, B., Diehl, H. T., Honscheid, K., et al. 2015, AJ, 150, 150

Garcia, A., Morgan, R., Herner, K., et al. 2020, ApJ, 903, 75

Gillanders, J. H., Smartt, S. J., Sim, S. A., Bauswein, A., & Goriely, S. 2022, MNRAS, 515, 631

Goldstein, A., Veres, P., Burns, E., et al. 2017, ApJ, 848, L14

Goldstein, D. A., Andreoni, I., Nugent, P. E., et al. 2019, ApJ, 881, L7
R.M. thanks the LSSTC Data Science Fellowship Program, which is funded by LSSTC; NSF Cybertraining grant No. 1829740; the Brinson Foundation; and the Moore Foundation.

A.P. acknowledges that support for this work was provided by NASA through NASA Hubble Fellowship grant HST-HF2-51488.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555.

L.S.-S. acknowledges financial support from FAPESP through grant No. 2020/03301-5.

MESP is funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) under Germany's Excellence Strategy—EXC 2121 “Quantum Universe”—390833306.

F.O.E. acknowledges financial support from FONDECYT grant No. 1201223.

The authors made use of Sci-Mind servers machines developed by the CBPF AI LAB team and would like to thank P. Russano and M. Portes de Albuquerque for all the support in infrastructure matters.

## ORCID iDs

C. R. Bom https://orcid.org/0000-0003-4383-2969

J. Annis https://orcid.org/0000-0002-0609-3987

A. Garcia https://orcid.org/0000-0001-9578-6322

A. Palmese https://orcid.org/0000-0002-6011-0530

N. Sherman https://orcid.org/0000-0001-5399-0114

M. Soares-Santos https://orcid.org/0000-0001-6082-8529

L. Santana-Silva https://orcid.org/0000-0003-3402-6164

R. Morgan https://orcid.org/0000-0002-7016-5471

K. Bechtol https://orcid.org/0000-0001-8156-0429

T. Davis https://orcid.org/0000-0003-4932-9379

H. T. Diehl https://orcid.org/0000-0002-8357-7467

S. S. Allam https://orcid.org/0000-0002-7069-7857

T. G. Bachmann https://orcid.org/0000-0002-6119-5353

J. García-Bellido https://orcid.org/0000-0002-9370-8360

M. S. S. Gill https://orcid.org/0000-0003-2524-5154

K. Herner https://orcid.org/0000-0001-6718-2978

M. Makler https://orcid.org/0000-0003-2206-2651

F. Olivares E. https://orcid.org/0000-0002-5115-6377

M. E. S. Pereira https://orcid.org/0000-0002-7131-7684

D. L. Tucker https://orcid.org/0000-0001-7211-5729

M. P. Wiesner https://orcid.org/0000-0001-8653-7738

M. Aguena https://orcid.org/0000-0001-5679-6747

O. Alves https://orcid.org/0000-0002-7394-9466

D. Bacon https://orcid.org/0000-0002-2562-8537

P. H. Bernardinelli https://orcid.org/0000-0003-0743-9422

S. Bocquet https://orcid.org/0000-0002-4900-805X

D. Brooks https://orcid.org/0000-0002-8458-5047

M. Carrasco Kind https://orcid.org/0000-0002-4802-3194

J. Carretero https://orcid.org/0000-0002-3130-0204

C. Conselice https://orcid.org/0000-0003-1949-7638

J. De Vicente https://orcid.org/0000-0001-8318-6813

S. Desai https://orcid.org/0000-0002-0466-3288

S. Everett https://orcid.org/0000-0002-3745-2882

I. Ferrero https://orcid.org/0000-0002-1295-1132

J. Frieman https://orcid.org/0000-0003-4079-3263

D. W. Gerdes https://orcid.org/0000-0001-6942-2736

D. Gruen https://orcid.org/0000-0003-3270-7644

R. A. Gruendl https://orcid.org/0000-0002-4588-6517

G. Gutierrez https://orcid.org/0000-0003-0825-0517

S. R. Hinton https://orcid.org/0000-0003-2071-9349

D. L. Hollowood https://orcid.org/0000-0002-9369-4157

K. Honscheid https://orcid.org/0000-0002-6550-2023

D. J. James https://orcid.org/0000-0001-5160-4486

K. Kuehn https://orcid.org/0000-0003-0120-0808

N. Kuropatkin https://orcid.org/0000-0003-2511-0946

P. Melchior https://orcid.org/0000-0002-8873-5065

J. Mena-Fernández https://orcid.org/0000-0001-9497-7266

F. Menteau https://orcid.org/0000-0002-1372-2534

A. Piereas https://orcid.org/0000-0001-9186-6042

A. A. Plazas Malagón https://orcid.org/0000-0002-2598-0514

E. Sanchez https://orcid.org/0000-0002-9646-8198

I. Sevilla-Noarbe https://orcid.org/0000-0002-1831-1953

M. Smith https://orcid.org/0000-0002-3321-1432

E. Suchyta https://orcid.org/0000-0002-7047-9358

G. Tarle https://orcid.org/0000-0003-1704-0781

C. To https://orcid.org/0000-0001-7836-2261

N. Weaverdyck https://orcid.org/0000-0001-9382-5199

## References

Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017a, ApJL, 848, L13

Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017b, ApJL, 848, L12

Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2018, LRR, 21, 3

Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2020, LRR, 23, 3

Abbott, R., Abbott, T. D., Abraham, S., et al. 2021, ApJL, 915, L5

Abbott, R., Abbott, T. D., Acernese, F., et al. 2023, PhRvX, 13, 011048

Abdalla, E., Abellán, G. F., Aboubrahim, A., et al. 2022, JHEAp, 34, 49

Anand, S., Coughlin, M. W., Kasliwal, M. M., et al. 2021, NatAs, 5, 46

Andreoni, I., Ackley, K., Cooke, J., et al. 2017, PASA, 34, e069

Andreoni, I., Goldstein, D. A., Anand, S., et al. 2019, ApJL, 881, L16

Andreoni, I., Goldstein, D. A., Kasliwal, M. M., et al. 2020, ApJ, 890, 131

Arcavi, I., Hosseinzadeh, G., Howell, D. A., et al. 2017, Natur, 551, 64

Ashton, G., Ackley, K., Magaña Hernandez, I., & Piotrzkowski, B. 2021, CQGra, 38, 235004

Balasubramanian, A., Corsi, A., Mooley, K. P., et al. 2021, ApJL, 914, L20

Bom, C. R., & Palmese, A. 2023, arXiv:2307.01330

Bulla, M. 2019, MNRAS, 489, 5037

Buonanno, A., Iyer, B. R., Ochsner, E., Pan, Y., & Sathyaprakash, B. S. 2009, PhRvD, 80, 084043

Chase, E. A., O'Connor, B., Fryer, C. L., et al. 2022, ApJ, 927, 163

Chen, H.-Y., Cowperthwaite, P. S., Metzger, B. D., & Berger, E. 2021, ApJL, 908, L4

Chen, H.-Y., Fishbach, M., & Holz, D. E. 2018, Natur, 562, 545

Chen, H.-Y., & Holz, D. E. 2014, arXiv:1409.0522

Colombo, A., Salafia, O. S., Gabrielli, F., et al. 2022, ApJ, 937, 79

Coughlin, M. W., Dietrich, T., Antier, S., et al. 2020, MNRAS, 497, 1181

Coughlin, M. W., Dietrich, T., Doctor, Z., et al. 2019, MNRAS, 480, 3871

Coughlin, M. W., Dietrich, T., Margalit, B., & Metzger, B. D. 2019, MNRAS, 489, L91

Coulter, D., Foley, R., Kilpatrick, C., et al. 2017, Sci, 358, 1556

Cowperthwaite, P., Berger, E., Villar, V., et al. 2017, ApJL, 848, L17

Cowperthwaite, P. S., Villar, V. A., Scolnic, D. M., & Berger, E. 2019, ApJ, 874, 88

Darbha, S., & Kasen, D. 2020, ApJ, 897, 150

Darbha, S., Kasen, D., Foucart, F., & Price, D. J. 2021, ApJ, 915, 69

Del Pozzo, W. 2012, PhRvD, 86, 043011

Díaz, M. C., Macri, L. M., Garcia Lambas, D., et al. 2017, ApJL, 848, L29

Dietrich, T., & Ujevic, M. 2017, CQGra, 34, 105014

Drout, M. R., Piro, A. L., Shappee, B. J., et al. 2017, Sci, 358, 1570

Drozda, P., Belczynski, K., O'Shaughnessy, R., Bulik, T., & Fryer, C. L. 2022, A&A, 667, A126

Finke, A., Foffa, S., Iacovelli, F., Maggiore, M., & Mancarella, M. 2021, JCAP, 2021, 026

Finn, L. S., & Chernoff, D. F. 1993, PhRvD, 47, 2198

Flaugher, B., Diehl, H. T., Honscheid, K., et al. 2015, AJ, 150, 150

Garcia, A., Morgan, R., Herner, K., et al. 2020, ApJ, 903, 75

Gillanders, J. H., Smartt, S. J., Sim, S. A., Bauswein, A., & Goriely, S. 2022, MNRAS, 515, 631

Goldstein, A., Veres, P., Burns, E., et al. 2017, ApJL, 848, L14

Goldstein, D. A., Andreoni, I., Nugent, P. E., et al. 2019, ApJL, 881, L7
Gottlieb, O., Nakar, E., & Piran, T. 2018, MNRAS, 473, 576

Graham, M. J., Ford, K. E. S., McKernan, B., et al. 2020, PhRvL, 124, 251102

Gray, R., Beirnaert, F., Karathanasis, C., et al. 2023, JCAP, 2023, 023

Herner, K., Annis, J., Brout, D., et al. 2020, A&C, 33, 100425

Hotokezaka, K., & Nakar, E. 2020, ApJ, 891, 152

Kasen, D., Metzger, B., Barnes, J., Quataert, E., & Ramirez-Ruiz, E. 2017, Natur, 551, 80

Kawaguchi, K., Shibata, M., & Tanaka, M. 2020a, ApJ, 893, 153

Kawaguchi, K., Shibata, M., & Tanaka, M. 2020b, ApJ, 889, 171

Kessler, R., Bernstein, J. P., Cinabro, D., et al. 2009, PASP, 121, 1028

Kessler, R., Narayan, G., Avelino, A., et al. 2019, PASP, 131, 094501

Kilpatrick, C. D., Coulter, D. A., Arcavi, I., et al. 2021, ApJ, 923, 258

Kilpatrick, C. D., Fong, W.-f., Blanchard, P. K., et al. 2022, ApJ, 926, 49

Kilpatrick, C. D., Foley, R. J., Kasen, D., et al. 2017, Sci, 358, 1583

Knox, L., & Millea, M. 2020, PhRvD, 101, 043533

LIGO Scientific Collaboration, 2020 LALSuite: LIGO Scientific Collaboration Algorithm Library Suite, Astrophysics Source Code Library, ascl:2012.021

LIGO Scientific Collaboration, Virgo Collaboration 2021, ApJ, 909, 218

LIGO Scientific Collaboration, Virgo Collaboration, KAGRA Collaboration 2023, PhRvX, 13, 041039

Lipunov, V. M., Gorbovskoy, E., Kornilov, V. G., et al. 2017, ApJL, 850, L1

Morgan, R., Soares-Santos, M., Annis, J., et al. 2020, ApJ, 901, 83

Nicholl, M., Berger, E., Kasen, D., et al. 2017, ApJL, 848, L18

Neilsen, H., Bernstein, G., Gruendl, R., & Kent, S. 2016, Limiting Magnitude, τ, teff, and Image Quality in DES Year 1 FERMILAB-TM-2610-AE-CD 1450750, Fermi National Accelerator Lab. (FNAL)

Nissanke, S., Holz, D. E., Hughes, S. A., Dalal, N., & Sievers, J. L. 2010, ApJ, 725, 496

Oates, S. R., Marshall, F. E., Breeveld, A. A., et al. 2021, MNRAS, 507, 1296

O'Donnell, J. E. 1994, ApJ, 422, 158

Palmese, A., Bom, C. R., Mucesh, S., & Hartley, W. 2023, ApJ, 943, 56

Palmese, A., deVicente, J., Pereira, M. E. S., et al. 2020, ApJL, 900, L33

Palmese, A., Fishbach, M., Burke, C. J., Annis, J., & Liu, X. 2021b, ApJL, 914, L34

Petrov, P., Singer, L. P., Coughlin, M. W., et al. 2022, ApJ, 924, 54

Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6

Riess, A. G., Casertano, S., Yuan, W., et al. 2021, ApJL, 908, L6

Sagués Carracedo, A., Bulla, M., Feindt, U., & Goobar, A. 2021, MNRAS, 504, 1294

Savchenko, V., Ferrigno, C., Kuulkers, E., et al. 2017, ApJL, 848, L15

Schöneberg, N., Abellán, G. F., Sánchez, A. P., et al. 2022, PhR, 984, 1

Schutz, B. F. 1986, Natur, 323, 310

Schutz, B. F. 2011, CQGra, 28, 125023

Setzer, C. N., Peiris, H. V., Korobkin, O., & Rosswog, S. 2023, MNRAS, 520, 2829

Shandonay, A., Morgan, R., Bechtol, K., et al. 2022, ApJ, 925, 44

Singer, L. P., & Price, L. R. 2016, PhRvD, 93, 024013

Singer, L. P., Chen, H.-Y., Holz, D. E., et al. 2016a, ApJL, 829, L15

Singer, L. P., Chen, H.-Y., Holz, D. E., et al. 2016b, ApJS, 226, 10

Soares-Santos, M., Holz, D. E., Annis, J., et al. 2017, ApJL, 848, L16

Soares-Santos, M., Palmese, A., Hartley, W., et al. 2019, ApJL, 876, L7

Stewart, A. R., Lo, L.-T., Korobkin, O., et al. 2022, arXiv:2201.01865

Thielemann, F. K., Eichler, M., Panov, I. V., & Wehmeyer, B. 2017, ARNPS, 67, 253

Tucker, D. L., Wiesner, M. P., Allam, S. S., et al. 2022, ApJ, 929, 115

Utsumi, Y., Tanaka, M., Tominaga, N., et al. 2017, PASJ, 69, 101

Valentino, E. D., Mena, O., Pan, S., et al. 2021, CQGra, 38, 153001

Villar, V. A., Guillochon, J., Berger, E., et al. 2017, ApJL, 851, L21

Wollaeger, R. T., Fryer, C. L., Chase, E. A., et al. 2021, ApJ, 918, 10

Zhu, J.-P., Wu, S., Yang, Y.-P., et al. 2021, ApJ, 921, 156

Zhu, J.-P., Wu, S., Yang, Y.-P., et al. 2023, ApJ, 942, 88
Gottlieb, O., Nakar, E., & Piran, T. 2018, MNRAS, 473, 576

Graham, M. J., Ford, K. E. S., McKernan, B., et al. 2020, PhRvL, 124, 251102

Gray, R., Beirnaert, F., Karathanasis, C., et al. 2023, JCAP, 2023, 023

Herner, K., Annis, J., Brout, D., et al. 2020, A&C, 33, 100425

Hotokezaka, K., & Nakar, E. 2020, ApJ, 891, 152

Kasen, D., Metzger, B., Barnes, J., Quataert, E., & Ramirez-Ruiz, E. 2017, Natur, 551, 80

Kawaguchi, K., Shibata, M., & Tanaka, M. 2020a, ApJ, 893, 153

Kawaguchi, K., Shibata, M., & Tanaka, M. 2020b, ApJ, 889, 171

Kessler, R., Bernstein, J. P., Cinabro, D., et al. 2009, PASP, 121, 1028

Kessler, R., Narayan, G., Avelino, A., et al. 2019, PASP, 131, 094501

Kilpatrick, C. D., Coulter, D. A., Arcavi, I., et al. 2021, ApJ, 923, 258

Kilpatrick, C. D., Fong, W.-f., Blanchard, P. K., et al. 2022, ApJ, 926, 49

Kilpatrick, C. D., Foley, R. J., Kasen, D., et al. 2017, Sci, 358, 1583

Knox, L., & Millea, M. 2020, PhRvD, 101, 043533

LIGO Scientific Collaboration, 2020 LALSuite: LIGO Scientific Collaboration Algorithm Library Suite, Astrophysics Source Code Library, ascl:2012.021

LIGO Scientific Collaboration, Virgo Collaboration 2021, ApJ, 909, 218

LIGO Scientific Collaboration, Virgo Collaboration, KAGRA Collaboration 2023, PhRvX, 13, 041039

Lipunov, V. M., Gorbovskoy, E., Kornilov, V. G., et al. 2017, ApJL, 850, L1

Morgan, R., Soares-Santos, M., Annis, J., et al. 2020, ApJ, 901, 83

Nicholl, M., Berger, E., Kasen, D., et al. 2017, ApJL, 848, L18

Neilsen, H., Bernstein, G., Gruendl, R., & Kent, S. 2016, Limiting Magnitude, τ, teff, and Image Quality in DES Year 1 FERMILAB-TM-2610-AE-CD 1450750, Fermi National Accelerator Lab. (FNAL)

Nissanke, S., Holz, D. E., Hughes, S. A., Dalal, N., & Sievers, J. L. 2010, ApJ, 725, 496

Oates, S. R., Marshall, F. E., Breeveld, A. A., et al. 2021, MNRAS, 507, 1296

O'Donnell, J. E. 1994, ApJ, 422, 158

Palmese, A., Bom, C. R., Mucesh, S., & Hartley, W. 2023, ApJ, 943, 56

Palmese, A., deVicente, J., Pereira, M. E. S., et al. 2020, ApJL, 900, L33

Palmese, A., Fishbach, M., Burke, C. J., Annis, J., & Liu, X. 2021b, ApJL, 914, L34

Petrov, P., Singer, L. P., Coughlin, M. W., et al. 2022, ApJ, 924, 54

Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6

Riess, A. G., Casertano, S., Yuan, W., et al. 2021, ApJL, 908, L6

Sagués Carracedo, A., Bulla, M., Feindt, U., & Goobar, A. 2021, MNRAS, 504, 1294

Savchenko, V., Ferrigno, C., Kuulkers, E., et al. 2017, ApJL, 848, L15

Schöneberg, N., Abellán, G. F., Sánchez, A. P., et al. 2022, PhR, 984, 1

Schutz, B. F. 1986, Natur, 323, 310

Schutz, B. F. 2011, CQGra, 28, 125023

Setzer, C. N., Peiris, H. V., Korobkin, O., & Rosswog, S. 2023, MNRAS, 520, 2829

Shandonay, A., Morgan, R., Bechtol, K., et al. 2022, ApJ, 925, 44

Singer, L. P., & Price, L. R. 2016, PhRvD, 93, 024013

Singer, L. P., Chen, H.-Y., Holz, D. E., et al. 2016a, ApJL, 829, L15

Singer, L. P., Chen, H.-Y., Holz, D. E., et al. 2016b, ApJS, 226, 10

Soares-Santos, M., Holz, D. E., Annis, J., et al. 2017, ApJL, 848, L16

Soares-Santos, M., Palmese, A., Hartley, W., et al. 2019, ApJL, 876, L7

Stewart, A. R., Lo, L.-T., Korobkin, O., et al. 2022, arXiv:2201.01865

Thielemann, F. K., Eichler, M., Panov, I. V., & Wehmeyer, B. 2017, ARNPS, 67, 253

Tucker, D. L., Wiesner, M. P., Allam, S. S., et al. 2022, ApJ, 929, 115

Utsumi, Y., Tanaka, M., Tominaga, N., et al. 2017, PASJ, 69, 101

Valentino, E. D., Mena, O., Pan, S., et al. 2021, CQGra, 38, 153001

Villar, V. A., Guillochon, J., Berger, E., et al. 2017, ApJL, 851, L21

Wollaeger, R. T., Fryer, C. L., Chase, E. A., et al. 2021, ApJ, 918, 10

Zhu, J.-P., Wu, S., Yang, Y.-P., et al. 2021, ApJ, 921, 156

Zhu, J.-P., Wu, S., Yang, Y.-P., et al. 2023, ApJ, 942, 88