A comprehensive simulation framework for multi-modal kilonova observations from all-sky surveys

Andrew Toivonen; Farhana Taiyebah; Felipe Fontinele Nunes; Leonard Lupin-Jimenez; Malhar Kulkarni; Michael W. Coughlin; Skylar Callis; Soumi De

REVIEW

A comprehensive simulation framework for multi-modal kilonova observations from all-sky surveys

Felipe Fontinele Nunes, Andrew Toivonen, Farhana Taiyebah, Leonard Lupin-Jimenez, Skylar Callis, Malhar Kulkarni, Soumi De, Michael W. Coughlin

Instrumentation and Methods for Astrophysics · Submitted 1 Oct 2026 · 296 references

Pith's verdict: A well-built and honestly validated simulator for multimodal kilonova training data whose real limit is the external realism of the LANLTP2 model family — a limit the authors themselves document clearly.

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CitationAndrew Toivonen, Farhana Taiyebah, Felipe Fontinele Nunes, Leonard Lupin-Jimenez, Malhar Kulkarni, Michael W. Coughlin, Skylar Callis, Soumi De. "A comprehensive simulation framework for multi-modal kilonova observations from all-sky surveys." arXiv:2610.02088 (2026). https://pith.science/paper/TVNQ6VJL

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Editor's note

read the letter

Read it. This is the most complete packaged pipeline for synthetic kilonova observations I've seen in one place: photometry via SVD surrogate, spectra via a neural emulator with POD residual correction, and images injected into real archival ZTF science/reference pairs. Nothing here is individually new — SVD surrogates (Pang et al.), spectral emulators (Lukosiute et al., Ristic et al.), image injection (Kessler et al.), data-driven cadence (Sravan et al.) are all established — so the contribution is the integration and the validation. That's a legitimate kind of contribution, and the question is whether the integrated product is trustworthy. Mostly it is.

The image closure is the strongest part: RMS 0.051 mag over 2,304 injections, and the replay of a real ZTF light curve (ZTF18abfhaji) recovers at 0.019/0.027 mag on genuinely independent exposures. The spectral emulator is validated the right way — held out at the level of complete ejecta configurations (128 configurations, 228,096 spectra), not individual spectra, so correlated sequences don't leak into the test set, with mean E_shape 0.0465. The photometric surrogate is weaker: median |dM| = 0.323 mag, RMS 0.763 mag, degrading sharply near edge-on viewing. The authors argue this is the faint, unlikely-to-be-detected corner of parameter space, which is fair, but anyone training a parameter-estimation net on this data carries a third-of-a-magnitude floor.

The load-bearing concern, and I agree with the stress-test note here, is that all three modalities derive from the LANLTP2/SuperNu family, so that family's external fidelity limits the whole pipeline. The paper's own Section 6.2 comparison shows the gap: E_shape 0.340 against AT2017gfo versus 0.0465 against held-out library spectra, plus the observed late-time 2.1 micron feature the model doesn't reproduce. The authors flag this themselves (b621, b622, b631) and warn that synthetic spectra will teach parameter-estimation models the model's coherent spectral biases. One caveat cuts both ways and is acknowledged (b629): the comparison fixes ejecta parameters at the photometric MLE and optimizes nothing against the spectra, so the 0.340 conflates model physics with an unfavorable parameter choice. The bias is real; its magnitude is not cleanly established. The 7x emulator-vs-data error gap is genuine evidence that the bottleneck is the physics, not the surrogate.

Two smaller gripes. The abstract promises a comprehensive dataset and code, but neither ships here — I found no data availability section. And the Section 6.1 light-curve "sanity check" mostly validates the survey forward model given the photometric MLE, not the kilonova physics; the spectral comparison is where the real information is.

This deserves a serious referee and conditional acceptance after the code and dataset question is resolved. It's a tool paper — a 6, not a breakthrough — but a well-built and honest one. I'd bring it to reading group and cite it if I do LSST-era kilonova simulation work.

Referee report

Significance

The framework itself is well constructed and the validation is unusually thorough and honest. The image-closure test directly validates the injection operator on real survey images, the spectral emulator's held-out test is correctly grouped at the ejecta-configuration level (avoiding leakage between correlated epochs and lines of sight), and the paper explicitly documents its own limitations, including the near-edge-on photometric degradation and the external-fidelity gap against AT2017gfo. The machine-checked validation numbers (RMS 0.051 mag image closure, mean E_shape 0.0465 on 128 held-out configurations) are specific and reproducible. The central claim, however, is that the pipeline produces 'realistic' multimodal observations suitable for training downstream AI models, and that claim is only partially supported: the paper's own Section 6.2 establishes that the spectroscopic modality is limited by coherent bias in the underlying LANLTP2 family, not by surrogate error, and the photometric surrogate's median 0.323 mag error is unquantified for the detectable population.

Major objections (3)

[Abstract and Section 6.2 (Eq. 88); Section 7] The abstract (sentence b4) and Section 7 ('we validate our simulation methods and find they accurately produce realistic survey detections') claim 'realistic' observations, but the manuscript's own external anchor, AT2017gfo, shows a roughly seven-fold gap between data-level and held-out spectral error: median E_shape = 0.340 against observed spectra versus 0.0465 against held-out LANLTP2 calculations, a missing late-time 2.1 micron feature, and a broad optical maximum at 0.62 micron that the model places near 0.41 micron. The authors acknowledge this gap (b621, b622, b629–b631), but the framing in the abstract and discussion still overstates the result. Recommend recalibrating the central claim to 'consistent with the LANLTP2 radiative-transfer family' and stating in the abstract that synthetic spectra inherit the model family's coherent spectral bias; this is a load-bearing correction because the stated purpose is training parameter-estimation models that would absorb that bias. The authors acknowledge this: “These examples suggest that models selected through broadband criteria can retain large, coherent wavelength-dependent errors.”
[Section 5.2, Figure 10; Appendix A] The photometric surrogate, which is the backbone of the photometry modality, has median |dM| = 0.323 mag and RMS 0.763 mag over the validation sample, with MSE rising to 53 mag^2 near edge-on viewing angles. The argument that the worst regime is undetectable (b459) is reasonable, but the paper never reports surrogate accuracy restricted to the detectable population — e.g., events with a minimum number of epochs above the empirical ZTF limiting magnitude. Because the stated downstream use is parameter estimation, the reader needs this conditioning to judge whether the 0.323 mag median is representative of the training distribution; without it, the fidelity of the photometric modality for training is unquantified.
[Section 3.2, Eq. (56)] The augmentation amplitude sigma_model is a free parameter that materially broadens the generated light-curve distribution at fixed ejecta parameters, yet the manuscript never assigns it a value or justifies a choice, and the validation numbers in Section 5.2 are computed without clarifying whether sigma_model was active. Since Equation (56) controls how much the training data depart from the surrogate templates, the pipeline's noise budget is underspecified; a stated default value and a sensitivity check would make the generated datasets reproducible.

Minor objections (5)

[Figure 8(c) caption] The caption of Figure 8(c) contains a stray Zoom meeting URL ('https://umn.zoom.us/j/8070507201') that must be removed before submission.
[Abstract and Figures 2, 5] The package name 'kilonova-multimodal-emulator' appears without monospace formatting in the abstract, and the captions of Figures 2 and 5 contain the typo 'infered' for 'inferred'.
[Section 5.3 / 6 transition (sentence b565)] The sentence beginning 'Bins below 10^-3 ...' appears as a fragment at the join between Sections 5.3 and 6.1; it reads like a detached piece of a preceding paragraph and should be either completed or merged with the population-residual discussion.
[References] The reference list contains duplicated and inconsistently formatted entries (e.g., 'Abbott et al. 2017a' in two forms, and Wollaeger et al. 2021 listed as both a Zenodo record and an ApJ article); these should be consolidated.
[Eqs. (9) and (45)] The intrinsic parameter vector for the spectral emulator (Eq. 9) excludes the viewing angle, while the photometric surrogate (Eq. 45) includes it, with time and angle handled differently in each; a sentence clarifying this notational asymmetry would prevent reader confusion.

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The paper

The paper claims

The paper presents `kilonova-multimodal-emulator`, a pipeline that generates realistic ZTF-style kilonova observations in three modalities at once — photometry, spectra, and images — from a single radiative-transfer model family, designed…

The reading

With only one kilonova observed in detail, AI models meant to find kilonovae in the coming torrent of survey alerts have almost no real data to train on. This paper argues that a single simulation pipeline can manufacture realistic ZTF-style observations in three modalities at once — light curves, spectra, and images — all derived from the same radiative-transfer model family. Survey realism is imposed by historical ZTF cadence, depth, and photometric-uncertainty distributions, while images are made by injecting the synthetic source directly into archival ZTF exposures with exposure-specific point-spread functions and zero points. The pipeline's image component recovers injected sources to RMS 0.051 mag over 2,304 realizations, and simulated AT2017gfo-like light curves fall within the 90% credible interval of the measured GW170817 distance. If the simulated data is faithful, it supplies the training set needed for kilonova identification and parameter estimation in the LSST alert stream.

What carries the argument

The load-bearing object is the LANLTP2 two-component radiative-transfer model family — toroidal dynamical ejecta plus a peanut-shaped wind with electron fraction Y_e = 0.27, computed with the SuperNu Monte Carlo code — which supplies the intrinsic emission for all three modalities. Around it, three surrogates carry the argument: a neural spectral emulator combining a wavelength-local branch, a global latent branch, a Fourier neural operator, and a rank-160 proper-orthogonal-decomposition correction of residuals to interpolate spectra across ejecta mass, velocity, time, and viewing angle; a truncated-SVD light-curve surrogate predicting g, r, i magnitudes; and an image-injection operator that places the source into archival ZTF science exposures with exposure-specific PSFs and zero points, paired against aligned reference stamps. A survey forward model built from empirical ZTF cadence and limiting-magnitude distributions converts the intrinsic curves into realistic detection/upper-limit time series through a censored-observation likelihood.

Extended reading notes

Core claim

The central claim is that realistic, multimodal kilonova observations — photometry, spectra, and images — matched to a real survey (ZTF) can be generated from one radiative-transfer model family, fast enough to build datasets for large AI models. The intrinsic emission stays fixed across modalities: the LANLTP2 two-component model family (toroidal dynamical ejecta plus a lanthanide-rich wind, computed with the SuperNu Monte Carlo code) supplies both the light curves and the spectral sequences, an emulator predicts spectra at arbitrary ejecta parameters, and the image stage injects the same source into archival ZTF science frames using exposure-specific PSFs and zero points. Validation on held-out data shows the image injection-recovery reaches RMS 0.051 mag, the photometric surrogate achieves median |Δm| = 0.323 mag on grid interiors, and the spectral emulator reaches mean E_shape = 0.0465 against 128 held-out ejecta configurations; against AT2017gfo, the simulated light curves are consistent with the 90% credible interval.

Load-bearing premise

The entire simulated dataset inherits its emission from the LANLTP2 radiative-transfer model family, so if that physics model misses real kilonova spectral structure, the same bias enters every mode of the training data regardless of how accurate the surrogates are.

Editorial extensions

If this is right

A large simulated dataset from this pipeline can support training of AI classifiers and amortized parameter estimators for kilonovae, addressing the shortage of real observations.
Because the intrinsic models are interchangeable, the same survey forward model can generate multimodal training data for other transient classes.
The data-driven ZTF survey model — cadence, depth, and photometric uncertainties from historical observations — can be rerun for other surveys, notably LSST.
Retaining science/reference image stamp pairs alongside photometry and spectra gives downstream models practice with real host-galaxy morphology, crowding, and background rather than idealized point sources.
The censored-observation likelihood in Equation (62) could serve directly as the data model for likelihood-based parameter estimation on real ZTF light curves.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The gap between the emulator's internal fidelity (E_shape = 0.0465 against held-out library spectra) and its external fidelity (E_shape = 0.340 against AT2017gfo) suggests the bottleneck is now the physics model, not the surrogate — further emulator improvements will not close the distance to real kilonovae.
Extending the same architecture to LSST with Rubin's own cadence and depth distributions would be a natural next step, generating training data for the primary alert stream rather than a follow-up facility.
A joint spectroscopic and photometric fit within the LANLTP2 family, as the authors suggest, might reduce the AT2017gfo discrepancy by shifting ejecta parameters, but would not resolve the missing late-time 2.1 µm feature, which appears to be a genuine limitation of the model family.
The generic image-injection operator and the decoupled host prior mean the pipeline could be repurposed for any unresolved transient source whose light curve is supplied, independent of kilonova physics.

Original abstract

Current all-sky surveys such as Vera Rubin's Legacy Survey of Space and Time and the Zwicky Transient Facility (ZTF) promise a wealth of scientific gains that are contained in the millions of astrophysical transient candidates produced each night. Kilonovae, one such transient of interest, will be challenging to identify in the alert stream and will require efficient artificial intelligence models to parse the large, real-time influx of data. In order to build these large models, comprehensive multimodal datasets are necessary for training. Due to a lack of numerous kilonova observations, we propose $\texttt{kilonova-multimodal-emulator}$ -- a simulation pipeline for realistic, multimodal kilonova observations comprised of photometry, spectra, and images. We demonstrate this pipeline for ZTF-type observations, based on historical cadence and limiting magnitude information from the Bright Transient Survey (BTS) and using the latest radiative transfer kilonova models to produce a comprehensive dataset meant for the training of large artificial intelligence model.

The compiled record

The paper's own sentences, arranged, kept beside the review and separate from it. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-10-02: 1 request, 4,452 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.33 · human participants 0.02 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.10 · compares with prior results 0.06 · mentions code or data availability 0.12.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

Current all-sky surveys such as Vera Rubin's Legacy Survey of Space and Time and the Zwicky Transient Facility (ZTF) promise a wealth of scientific gains that are contained in the millions of astrophysical transient candidates produced each night. Kilonovae, one such transient of interest, will be challenging to identify in the alert stream and will require efficient artificial intelligence models to parse the large, real-time influx of data. In order to build these large models, comprehensive multimodal datasets are necessary for training. Due to a lack of numerous kilonova observations, we propose $\texttt{kilonova-multimodal-emulator}$ -- a simulation pipeline for realistic, multimodal kilonova observations comprised of photometry, spectra, and images. We demonstrate this pipeline for ZTF-type observations, based on historical cadence and limiting magnitude information from the Bright Transient Survey (BTS) and using the latest radiative transfer kilonova models to produce a comprehensive dataset meant for the training of large artificial intelligence model.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Current all-sky surveys such as Vera Rubin's Legacy Survey of Space and Time and the Zwicky Transient Facility (ZTF) promise a wealth of scientific gains that are contained in the millions of astrophysical transient candidates produced each night.	background 1.00	0.05	0.03	asserted 0.50
s1	Kilonovae, one such transient of interest, will be challenging to identify in the alert stream and will require efficient artificial intelligence models to parse the large, real-time influx of data.	background 0.90	0.06	0.05	asserted 0.67
s2	In order to build these large models, comprehensive multimodal datasets are necessary for training.	background 0.82	0.06	0.07	asserted 1.00
s3	Due to a lack of numerous kilonova observations, we propose $\texttt{kilonova-multimodal-emulator}$ -- a simulation pipeline for realistic, multimodal kilonova observations comprised of photometry, spectra, and images.	contribution 0.97	0.85	0.34	asserted 0.95
s4	We demonstrate this pipeline for ZTF-type observations, based on historical cadence and limiting magnitude information from the Bright Transient Survey (BTS) and using the latest radiative transfer kilonova models to produce a comprehensive dataset meant for the training of large artificial intelligence model.	method 0.87	0.92	0.21	asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.69 · evidence from simulation or computation 1.00.

What was examined, and what was not

sources read

arXiv listing abstract v1

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

Figures

**Figure 1.** Example of the spectroscopic data-generation chain at an injection position in NGC 772, at a projected offset of ≃ 42.9 ′′ from the galaxy center. Panel (a) shows the archival Pan-STARRS1 grizy cutouts centered on the selected sky position; these images provide the five-band measurements used by CIGALE to reconstruct the local host-galaxy spectrum entering Eq. (38). Panel (b) shows the transient sequence alone from the same SuperNu model family adopted for the photometric simulations, with viewing angle ι ≃ 19◦ , plotted in the native rest-frame 10 pc fλ normalization of the model…

**Figure 2.** Example model light-curve families in ZTF gri bands as a function of the mass of the wind ejecta log10 Mej,wind, an intrinsic parameter. Other intrinsic and extrinsic injection parameters are fixed and consistent with AT2017gfo as infered by the maximum likelihood estimate from B. L. King et al. (2025) for the LANLTP2 model. Curves are shown as apparent AB magnitudes. simple prescription for local variation around the templates, although physical changes in ejecta properties can produce correlated changes across time and wavelength. Setting σmodel = 0 recovers the unperturbed…

**Figure 3.** Survey-model components used to construct the synthetic ZTF observations. Left: empirical inter-visit spacing distributions for the public g/r survey cadence and the separately modeled i-band revisit cadence. Middle: single-epoch 5σ limiting-magnitude distributions measured from the public-program observations, shown separately in g, r, and i. Right: single-epoch 5σ limiting-magnitude distributions for 300 s ToO observations in g and r. separately for the relevant survey components and filters. For the ToO component, the limiting-magnitude distribution is conditioned on the longer…

**Figure 4.** Empirical ZTF photometric uncertainty model: reported magnitude uncertainty σm versus true magnitude for gri. The solid curves trace the median σm; shaded regions enclose the central 68% probability mass of the conditional distribution in Eq. (59). p(D | ϑ, ξ) = Y k∈Idet N

**Figure 5.** Example injected kilonova light curve (solid lines) and ZTF-like survey sampling (markers) in gri bands. Upper limits are shown as open inverted triangles. Bottom panel shows residuals mtrue − mobs for detections. Intrinsic and extrinsic injection parameters are consistent with AT2017gfo as infered by the maximum likelihood estimate from B. L. King et al. (2025) for the LANLTP2 model. generally, where synthetic point sources are overlaid on real survey images and interpreted relative to a static reference scene in order to assess detection and measurement performance (E. C. Bellm…

**Figure 6.** Single-epoch construction of one host-associated, matched ZTF science/reference postage-stamp pair. Panel (a) shows the selected archival science exposure before injection. The circular marker identifies the host center, the gold vector represents the sampled projected offset (R, ϕ), and its endpoint defines the injection coordinates (α⋆, δ⋆). The compass and 20-arcsec scale are derived from the science-image astrometric solution. Panel (b) shows the same exposure after adding the shifted, unit-normalized exposure PSF with the flux implied by the prescribed AB magnitude, I sci e,inj…

**Figure 7.** From a simulated kilonova realization to a multiband image sequence. The upper panel shows an AT2017gfo-like ZTF realization from the LANLTP2 model family, evaluated at the adopted literature distance of NGC 1614, D = 64 Mpc. Filled points with error bars denote simulated detections, open downward triangles denote simulated upper limits, and the solid curves show the underlying model light curves used to set the injected fluxes. Open squares mark the specific sampled visits used in the image construction below. The three image rows show, respectively, g-, r-, and i-band aligned…

**Figure 8.** Validation of the image-injection operator on real ZTF science exposures. Panel (a) shows six representative epochs from the ZTF18abfhaji replay. The upper row contains the archival science stamps before injection, and the lower row contains the same scenes after adding the source. Headers give the time since the first detection and the prescribed band-specific apparent magnitude, mg or mr. Each stamp is displayed with an inverse-hyperbolic-sine stretch referenced to the robust sky noise. The sequential maps share a neutral low-surface-brightness range and transition toward the…

**Figure 9.** Coverage of the four-dimensional intrinsic-parameter space used for the photometric-surrogate evaluation. Green triangles mark the original training-grid nodes, and red circles mark the 66 validation nodes. All of the validation nodes are located to the interior of a 2% margin on the edge of the parameter space to avoid edge cases. Off-diagonal panels show pairwise projections in log10 Mdyn ej , v dyn ej , log10 Mwind ej , and v wind ej . Diagonal panels show the marginal distributions of the selected configurations. Viewing angle is not shown here; each selected configuration is…

**Figure 10.** Photometric-surrogate accuracy in the three ZTF bands (g, r, i), restricted to the first 10 days after explosion. (a) Mean squared error between predicted and true AB magnitude versus viewing angle ι, evaluated over all available nodes at 54 viewing angles. Accuracy degrades sharply near edge-on viewing angles (ι ≈ 89◦ –91◦ ). (b) Predicted versus true absolute magnitude for the 66 selected interior configurations of

**Figure 11.** Surrogate-predicted (solid) and SuperNu (dashed) absolute-magnitude light curves for the median MSE intrinsic-parameter–viewing-angle pair from the 66 selected configurations shown in

**Figure 12.** Simulated LANLTP2 spectra and emulator reconstructions for an ejecta configuration excluded from fitting and model selection. The physical parameters are Md = 0.0577 M⊙, vd = 0.051c, Mw = 0.0218 M⊙, and vw = 0.116c. Six epochs are shown at µ = 0.50. The configuration was selected as the reserved simulation whose mean shape error lies closest to the median among all test simulations. The simulated spectra are plotted without smoothing or renormalization. The labels above the panels report the integrated normalized shape error from Equation (86). cle markers and the solid lines…

**Figure 13.** Population distribution of log10(f emu λ /f LANLTP2 λ ) over the 228,096 spectra in the reserved test sample. Panel (a) shows the median signed residual, panel (b) shows the median absolute residual, and panel (c) shows the 90th percentile of the absolute residual as functions of rest-frame wavelength and time after merger. Bins below 10−3 of the maximum simulated fλ in each spectrum are excluded before computing the population quantiles. spans 1.43 to 10.40 days after merger and covers the optical and near-infrared wavelength range. The released products have been flux…

**Figure 14.** Population diagnostics for the held-out LANLTP2 simulations. Panel (a) compares emulated and simulated log10 Lbol for all 228,096 test spectra. Panel (b) compares log10 pbj and log10 pj for 2,048,000 wavelength samples drawn equally from the 128 reserved ejecta configurations and restricted to bins with native fλ above 10−3 of the maximum for that spectrum. In both panels, color gives the number of samples in each hexagonal bin and the dashed line marks exact reconstruction. The displayed shape range contains the central 99.9% of the combined native and emulated sample. Panels (c)…

**Figure 15.** A comparison of AT2017gfo to simulated ZTF observations. The circle markers show the simulated ZTF observations and the solid lines are the accompanying light curves the individual observations are drawn from. The light curves are simulated using the maximum likelihood AT2017gfo parameters found in B. L. King et al. (2025). The shaded region corresponds to the 90% credible interval for a set of simulated kilonova light curves as seen by ZTF within the measured distance uncertainty of GW170817. The diamond markers show the AT2017gfo observations in ps1 bands ( Coulter et al. 2017;…

**Figure 16.** Multiepoch comparison of the LANLTP2 spectral emulator with the public spectra of AT2017gfo from 1.43 to 7.40 days after merger. Black curves show VLT/X-shooter observations at the labeled phases. Blue curves show the HST/WFC3 grism spectra at 4.9 and 7.3 days, plotted in the panels containing the nearest X-shooter epochs. Green curves show the emulator evaluated at the maximum-likelihood parameters from B. L. King et al. (2025), with ι = 0.75 rad and DL = 44 Mpc. The shaded region shows the range of simulated spectra obtained by varying the luminosity distance over the measured 26…

**Figure 17.** Surrogate-predictions (dashed) and training grid points (solid) absolute-magnitude light curves for the three highest mean MSE training grid points, all near (ι = 90◦ ). Panel titles report the viewing angle and ejecta configurations for each case. These non-smooth light curve shapes near the equatorial viewing angle are the origin of the high-MSE region identified in Figure 10a. Almualla, M., et al. 2021, Mon. Not. Roy. Astron. Soc., 504, 2822, doi: 10.1093/mnras/stab1090 Alsing, J., Peiris, H., Leja, J., et al. 2020, The Astrophysical Journal Supplement Series, 249, 5, doi:…

Figures reproduced from arXiv:2610.02088 by the authors.

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Discussion

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