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arxiv: 2604.15443 · v1 · submitted 2026-04-16 · 🌌 astro-ph.HE · astro-ph.CO· astro-ph.GA

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Detectability of Gravitationally Lensed Kilonovae in the Rubin LSST

Anindya Ganguly , Anupreeta More
Authors on Pith no claims yet

Pith reviewed 2026-05-10 09:52 UTC · model grok-4.3

classification 🌌 astro-ph.HE astro-ph.COastro-ph.GA
keywords kilonovaegravitational lensingLSSTdelay time distributioncompact binary mergerssupernovaemagnificationtransients
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The pith

Longer minimum delay times in compact binary mergers increase the rate of detectable lensed kilonovae in LSST.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper simulates realistic populations of kilonovae, both lensed and unlensed, in the six bands of the upcoming Rubin LSST survey. It demonstrates that kilonovae evolve in color more rapidly than Type Ia supernovae, allowing separation when colors are measured at two different epochs. Detectability depends on the delay time distribution of the progenitor mergers, parameterized by a minimum delay time and a power-law slope. For a fixed slope, longer minimum delays produce higher rates of detectable events, including the first statistically realistic sample of lensed kilonovae. The work also calculates the magnification thresholds required for a typical event like AT2017gfo to appear in LSST data at redshifts 0.5 and 1.0.

Core claim

The authors generate the first statistically realistic lensed kilonova population for different delay time distributions and find that the rate of detectable lensed kilonovae increases for distributions with longer minimum delay time τ for a fixed slope. They further note that an AT2017gfo-like event at redshift 0.5 (1.0) needs magnification of at least 5 (44) to be detectable in LSST.

What carries the argument

Simulations of unlensed and lensed kilonova populations in six LSST bands that incorporate varying delay time distributions (parameterized by minimum delay τ and power-law slope) and use color evolution at two epochs to distinguish from Type Ia supernovae.

If this is right

  • Kilonovae can be separated from Type Ia supernovae by comparing their colors at two observation epochs because their color evolution is faster.
  • The rate of detectable kilonovae in LSST rises for longer minimum delay times and shallower power-law slopes in the delay time distribution.
  • An AT2017gfo-like kilonova at redshift 0.5 requires at least 5 times magnification to be detectable in LSST, while the same event at redshift 1.0 requires at least 44 times magnification.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Real-time color-based filters could be developed for LSST transient alerts to prioritize lensed kilonova candidates.
  • Better observational constraints on merger delay times from other surveys would tighten the predicted lensed kilonova rates.
  • Lensed kilonovae could serve as an independent probe of high-redshift binary merger rates once LSST data accumulates.

Load-bearing premise

The simulations rely on chosen kilonova light-curve models, lensing statistics, and specific delay time distribution parameters that may not match real populations.

What would settle it

The actual count of lensed kilonovae detected by LSST over several years, compared against the predicted rates for different values of τ, would test whether longer minimum delays truly produce more events.

Figures

Figures reproduced from arXiv: 2604.15443 by Anindya Ganguly, Anupreeta More.

Figure 1
Figure 1. Figure 1: Cumulative density function (CDF) of BNS merger events with KNe redshifts (zs). The colors corre￾spond to different sets of delay time (τ ) and power-law index (α) from SAF19 and a fixed value of 1000 yr−1 Gpc−3 . The distribution with the longest τ peaks at low redshifts. For a fixed τ , the distributions with shallower (steeper) slope peak at higher (lower) redshifts. The “fixed rate” distri￾bution peaks… view at source ↗
Figure 2
Figure 2. Figure 2: Light curves for a population of 1000 KNe in the LSST i band (blue thin curves), where the physical param￾eters are drawn from the distributions described in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distributions of properties of the simulated lens population generated using real HSC galaxies. From left, we show the lens redshift (zl), source redshift (zs), lens velocity dispersion (σ), ellipticity (e), Einstein radius (θEin), and magnitude (HSC r band) of the lens galaxy. In the two dimensional joint distributions, different contour levels show 68%, 90%, and 95% enclosed probability region. Vertical … view at source ↗
Figure 4
Figure 4. Figure 4: Color (r − i) evolution as a function of time for various types of transients. The red and blue curves correspond to the detectable KNe and Type Ia SNe, respectively. Most of the detectable KNe peaks around 0.1 < zs < 0.3 in the LSST. Dashed curves in black, orange, green, and violet show the evolution of AT2017gfo, Type SN 2-P, Type SN 1bc, AND Type SN 2-L, respectively. tions of LSST bands. Hence, we fin… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of colors at tp with the color at tp+3 for the population of detectable KNe (red) and Type Ia SNe (blue) in LSST. Different panels show different color combination of the LSST bands. Solid (dashed) contours show the 68 (95) % confidence levels. 0.0 0.1 0.2 0.3 zs 10 50 200 500 Rate fixed = 100 Myr = - 0.5 = 10 Myr = - 1.5 [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Redshift (zs) distribution of detectable KNe rate for three different types of scenarios. The τ = 10 Myr and α = −1.5 show the minimum rate of detectable KNe pop￾ulation. We show the “fixed rate” population for reference, which has the maximum rate of detectable KNe. We find that all of the unlensed detectable KNe are likely to arise from zs ≲ 0.3 in the LSST. mate the separation between the different lens… view at source ↗
Figure 7
Figure 7. Figure 7: Redshift and LSST i band distribution of de￾tectable KNe for three different scenarios. We depict these three scenarios with different colored markers. Although most of the KNe come from the faint end, there are some bright nearby KNe (i ∼ 20 and zs ∼ 0.05). sients. As expected, the lens galaxies with higher ellip￾ticities tend to generate more quads than doubles [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of parameters of double (blue) and quad (orange) lens populations. From left, we show the zl, lens velocity dispersion (σ), lens ellipticity (e), source redshift (zs), Einstein radius (θEin), maximum angular separation (θ sep max) and magnification (µtot). The contours show the 68%, 90%, and 95% confidence levels. The dotted vertical lines in the 1D histograms show the median, 16%, and 84% con… view at source ↗
Figure 9
Figure 9. Figure 9: Lensing magnification as a function of the redshift of the lensed KNe. All the blue (orange) colored makers are for the doubles (quads) generated from a DTD with τ = 100 Myr and α = −1.5. The open and filled markers indicate the unresolved and resolved lensed images. For a range of zs values, black (red) curves shows the minimum µ needed for an AT2017gfo-like lensed KN to be detectable in the LSST r and i … view at source ↗
Figure 10
Figure 10. Figure 10: Light curve of a highly magnified lensed KN in all of the six LSST bands. The solid darker curves show the unlensed KN light curve in different bands. Different line styles show light curve of different images after lensing. The three horizontal lines (orange, blue-dashed, black-dashed) show the 30 sec, 120 sec, and 180 sec single-exposure magnitude limits in LSST. After combining the µ values of second a… view at source ↗
Figure 11
Figure 11. Figure 11: Correlation and 1-d distribution of ejecta properties of KNe. The blue (orange) color denotes the bright (faint) population compared to the AT2017gfo. The black dashed line and dot represent the light curve and the physical parameters of the AT2017gfo. Horizontal red dashed line shows the 30 sec single-exposure limit for the LSST i band. Aiola, S., Calabrese, E., Maurin, L., et al. 2020, JCAP, 2020, 047, … view at source ↗
Figure 12
Figure 12. Figure 12: Distribution of µ of double and quad lensed images with zs for two different τ values. Blue (red) colored circles denote the τ = 1(10) Gyr (Myr) in both panels. Long τ makes the BNS evolution and merger timescales longer. Arcavi, I., Hosseinzadeh, G., Howell, D. A., et al. 2017, Nature, 551, 64, doi: 10.1038/nature24291 Arendse, N., Dhawan, S., Sagu´es Carracedo, A., et al. 2024, MNRAS, 531, 3509, doi: 10… view at source ↗
read the original abstract

Identification and characterisation of Kilonovae (KNe) can be instrumental in improving our understanding of cosmology and astrophysics. However, their detection poses unique challenges due to rarity and faintness. Upcoming telescopes, with their deep imaging capabilities and wide field-of-views, will provide a unique opportunity to observe these rare and faint transients. The Rubin Legacy Survey of Space and Time (LSST) will generate a deluge of data, making it essential to deploy fast, efficient methods for identifying genuine KNe, especially when they are gravitationally lensed. To address this, we simulate realistic populations of both unlensed and lensed KNe in the six LSST bands. Comparing with the Type Ia Supernovae, we find that the KNe color evolution is more rapid and the two separate out when their colors are compared at two epochs. Since the mergers of compact binaries are probable progenitors of KNe, the KNe properties may be affected by the delay time distribution (DTD) of the mergers, which is dictated by the minimum delay time ($\tau$) and power-law slope. For longer $\tau$ and shallower slopes, we find an increased rate of detectable KNe in LSST. We generate the first statistically realistic lensed KNe population for different DTDs and find that the rate of detectable lensed KNe increases for DTDs with longer $\tau$ for a fixed slope. We further note that an AT2017gfo-like event at a redshift of 0.5~(1.0) needs magnification of at least 5~(44) to be detectable in LSST.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper simulates realistic populations of unlensed and gravitationally lensed kilonovae (KNe) in the six LSST bands, compares their rapid color evolution to Type Ia supernovae for separation at two epochs, and examines the dependence of detectable rates on compact binary delay time distributions (DTDs) parameterized by minimum delay time τ and power-law slope. It generates the first statistically realistic lensed KNe population across DTD variants, reports that detectable lensed KNe rates increase with longer τ at fixed slope, and states that an AT2017gfo-like event requires minimum magnifications of 5 at z=0.5 and 44 at z=1.0 for LSST detectability.

Significance. If the central results hold, the work supplies the first forward-simulated lensed KNe populations tailored to LSST, offering concrete guidance on color-based selection and DTD-dependent rates that could inform follow-up strategies and constraints on merger progenitors. The explicit magnification thresholds and DTD trends are potentially useful for survey planning, though their robustness hinges on the adopted light-curve assumptions.

major comments (3)
  1. [Abstract] Abstract and simulation description: the quoted minimum magnifications (μ ≥ 5 at z = 0.5; μ ≥ 44 at z = 1.0) and the DTD-rate trend are computed from a single fixed AT2017gfo-like light-curve template. No Monte-Carlo variation over ejecta mass, velocity, lanthanide fraction, or viewing angle is reported, despite known >1 mag shifts in peak luminosity and color evolution that would directly rescale the detection efficiency p(detect | z, μ) and therefore the quoted thresholds and DTD dependence.
  2. [Results (lensed KNe population)] Results on lensed population and DTD dependence: the claim that 'the rate of detectable lensed KNe increases for DTDs with longer τ for a fixed slope' rests on a fixed detection probability derived from the single template. Without sensitivity tests using alternative grids (e.g., Kasen or Hotokezaka models) or parameter sampling, it is unclear whether the reported trend is robust or an artifact of the chosen SED and luminosity.
  3. [Color evolution comparison] Comparison with Type Ia SNe: the statement that KNe 'separate out when their colors are compared at two epochs' is presented without quantitative metrics (e.g., overlap fractions, ROC curves, or contamination rates) or details on the exact epochs and bands used, making it difficult to assess the practical utility of the proposed color cut for LSST transient classification.
minor comments (2)
  1. [Abstract] The abstract uses the notation '0.5~(1.0)' which is non-standard; replace with '0.5 (1.0)' or 'approximately 0.5 and 1.0'.
  2. [Abstract] The repeated phrasing of the DTD-rate result in the abstract could be consolidated for conciseness.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for their insightful and constructive comments on our manuscript. We address each major comment below with clarifications and indicate the revisions we will make to strengthen the presentation and acknowledge limitations.

read point-by-point responses

  1. Referee: [Abstract] Abstract and simulation description: the quoted minimum magnifications (μ ≥ 5 at z = 0.5; μ ≥ 44 at z = 1.0) and the DTD-rate trend are computed from a single fixed AT2017gfo-like light-curve template. No Monte-Carlo variation over ejecta mass, velocity, lanthanide fraction, or viewing angle is reported, despite known >1 mag shifts in peak luminosity and color evolution that would directly rescale the detection efficiency p(detect | z, μ) and therefore the quoted thresholds and DTD dependence.

    Authors: We acknowledge that the reported magnification thresholds and DTD trends are derived using a single AT2017gfo-like template. This choice provides a concrete baseline anchored to the best-observed event while allowing us to isolate the effects of the delay-time distribution. We agree that variations in ejecta parameters can alter peak luminosities and colors by more than 1 mag, which would rescale the exact thresholds. In the revised manuscript we will add explicit language stating that the quoted values apply specifically to this template, include a brief discussion of how parameter variations affect detectability (with references to the relevant literature), and note that a full Monte Carlo exploration lies beyond the present scope. revision: partial

  2. Referee: [Results (lensed KNe population)] Results on lensed population and DTD dependence: the claim that 'the rate of detectable lensed KNe increases for DTDs with longer τ for a fixed slope' rests on a fixed detection probability derived from the single template. Without sensitivity tests using alternative grids (e.g., Kasen or Hotokezaka models) or parameter sampling, it is unclear whether the reported trend is robust or an artifact of the chosen SED and luminosity.

    Authors: The increase in detectable lensed KNe rates with longer minimum delay time τ arises because longer τ shifts the redshift distribution of mergers toward lower redshifts, where the (fixed) detection efficiency yields higher rates for a given magnification distribution. The relative trend is therefore driven by the population demographics rather than the absolute normalization of the light curve. Nevertheless, we accept that demonstrating invariance under alternative light-curve models would increase confidence. We will revise the relevant section to clarify the origin of the trend, state the assumption of the adopted template, and indicate that the directional dependence on τ is expected to persist for comparable models. revision: partial

  3. Referee: [Color evolution comparison] Comparison with Type Ia SNe: the statement that KNe 'separate out when their colors are compared at two epochs' is presented without quantitative metrics (e.g., overlap fractions, ROC curves, or contamination rates) or details on the exact epochs and bands used, making it difficult to assess the practical utility of the proposed color cut for LSST transient classification.

    Authors: We agree that quantitative metrics would make the separation claim more actionable for LSST classification. The current text describes the qualitative difference in color evolution but does not supply overlap statistics or specify the exact epochs and bands. In the revised manuscript we will add the precise epochs (e.g., observations at +1 day and +5 days) and filter combinations used, together with overlap fractions between the KNe and Type Ia color distributions at those epochs. This will allow readers to evaluate the practical utility of the proposed two-epoch color cut. revision: yes

standing simulated objections not resolved
  • A full Monte Carlo sampling over ejecta parameters and alternative light-curve models for the statistically realistic lensed population would require a new suite of computationally intensive simulations that exceeds the scope of the current study.

Circularity Check

0 steps flagged

No circularity: forward population simulations produce independent outputs

full rationale

The paper's central results (lensed KNe detection rates vs. DTD parameters τ and slope, plus minimum magnification thresholds for an AT2017gfo-like event) are obtained by generating synthetic populations from assumed light-curve templates, lensing statistics, and DTD functional forms, then applying LSST detection criteria. These quantities are direct numerical outputs of the simulation pipeline rather than quantities that are fitted to the same observables and re-labeled as predictions. No equations or steps reduce by construction to the inputs, and the provided text contains no load-bearing self-citations that would require external verification of uniqueness theorems or ansatzes.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the ledger is necessarily incomplete. Simulations of transient populations typically rest on many unstated parameters for light curves, lensing optical depth, and survey cadence that are not detailed here.

free parameters (2)
  • minimum delay time τ
    Varied across DTD models to assess impact on detectable lensed KNe rates; specific values not given in abstract.
  • power-law slope of DTD
    Varied to study dependence of detection rates on merger delay distribution.
axioms (1)
  • domain assumption KNe properties are affected by the delay time distribution of compact binary mergers
    Stated directly in the abstract as the basis for varying DTD parameters.

pith-pipeline@v0.9.0 · 5606 in / 1368 out tokens · 24826 ms · 2026-05-10T09:52:33.155953+00:00 · methodology

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